Ethics in AI Education

Welcome to a discussion devoted to ethics in a high education. The latest in our growing series of seminars associated with the exciting new Institute for Ethics in A.I. at the University of Oxford. I'm Peter Milliken. Gilbert Ryul, fellow and professor of philosophy that Hartford College, Oxford.

And I'll be chairing tonight's event. As artificial intelligence increasingly impacts on so many aspects of our lives, there's recently been hugely increased awareness of the importance of putting ethical considerations at the centre of A.I. developments. But for this to happen in any sustainable and wide, widespread way, it's crucial that ethics becomes established as a key part of A.I. education.

So that is the focus of our seminar tonight. I'm delighted to be joined by three young academics who've put a lot of thought into this area, and they will speak in turn, after which we'll have a discussion and questions. First up is Milo Phillips Brown, currently at M.I.T., but soon to join us here at Oxford. And that's second is how a new web. He's been teaching ethics in the Department of Computer Science here. And finally, Max Van Keek, who's been working alongside Helena.

And we'll be focussing particularly on the challenges that such teaching involves. Each of our speakers will talk for about fifteen or twenty minutes, and the event as a whole will last for 90 minutes. So we'll have plenty of time for discussion. And you're very welcome to offer your own questions to the speakers.

So please do and please feel free to do this at any time. So if questions occur to you as the speakers are speaking, put them into the comments box on YouTube and in due course, I'll be bringing those into the discussion. So we'd love to hear from you. We'd love to learn from your ideas. OK, so first, as I said, is Milo Phillips Brown.

Milo is currently distinguished fellow in ethics and technology at M.I.T. and senior research fellow in digital ethics and governance that the Jain Family Institute. I'm delighted that Milo is going to be joining us shortly after Christmas as associate professor of philosophy and tutorial fellow at Jesus College.

So here's one of the important new appointments that's come about because of the new Institute for Ethics in I. As part of his job here, he will be teaching ethics in the Department of Computer Science. So he's absolutely on this key interface between philosophy and computer science, which we're so keen on here at Oxford. Milo's led various efforts across M.I.T. in creating ethics curricula for STEM students.

For example, he's developed and taught ethics material for a wide range of STEM classes, focus focussing especially on the ethics of technology and on developing two completely new courses. A workshop in ethical engineering and experiential ethics. So we're looking very forward, very much to seeing what Milo does when he comes and teaches ethics here in Oxford. Over to you, Marlee. Thanks, Peter. Really glad to be here and looking forward to this conversation with Elayna and Max.

So I'm going to share my screen now and give a little bit of a talk show. Has that has I look in news. OK, great. So I'm going to be talking about goals for ethical engineering, pedagogy, and in particular, the slogan to get students ready, willing and able to engineer ethically. OK, so as Peter alluded to, the news is bleak and familiar technologies are going awry all over the place and this is called this has caused many calls for action.

Prominent amongst them, that engineering students, computer science students in particular, need to be trained in ethics so that history does not repeat itself. So what are you really talking about today is what it is that we should be doing or can be doing and might have difficulty doing in reaching this goal. So here's a brief overview plan of the plan of the talk. So we start talking about the learning objectives for ethics, pedagogy, for engineering students.

How do we get students to be at that point where history doesn't repeat itself? And this question kind of attending to a carefully with that particular goal in mind reveals, I think, a different picture of what we ought to be doing in teaching ethics than sort of the traditional approach in engineering ethics more generally. And so that leads into the second topic, which is how it is that we reach these new learning objectives.

And so I'm going to talk a bit, give you a preview of the kinds of things that I've been doing with my collaborators across M.I.T. and then close to talk a bit more about what the obstacles are to reaching these objectives. All right. Let's jump in. Learning objectives.

So we said that they're sort of like the the long term goal is to have students graduate from their graduate programmes or undergraduate programmes and once they sort of enter the real world to not be creating the mistakes of the past. What that means is that we want them ultimately to be doing something to be engineering ethically. And so if they're going to be able to do, though, if they're going to do those things, then they need.

To be taught, we think, two different things. One is to be able to do the things that we want them to do. To be able to identify, address and communicate about the ethical dimensions of their work. And also to be ready and willing, which is to say motivated to do these things. So these we think of as the learning objectives, what we want our students to do is have the ability to do something and the willingness to do it if they just happen to know a lot about ethics.

But I've no idea how to translate that into your practise. If they're not able to practise ethical engineering, that I won't really matter. Nothing will change. Similarly, even if they're able know exactly what to do but aren't motivated to do it. Nothing will change. So this is what we're shooting for. How do we get there? How are we going to reach these objectives? Well, start with this idea of trying to get students able to engineer ethically.

What we think is that the best way to get this is the idea of teaching ethics as a skill. Quite literally, teaching them to be able to engineer ethically. So what does that mean exactly? Well, at the core of the approach is to teach concrete methods for ethical engineering, something that you can sit down in practise in designing a technology and take steps to understand what the ethical implications are and how to go forward. So here we draw on large research fields and programmes.

So the field of responsible innovation, for example, or research programmes in value sensitive design or participatory design. There are people who've been working on these questions for decades. What can you do in practise to build technologies that are more responsible? The answer to those questions are the things that we want to teach students, and indeed that's what we have been teaching students across M.I.T.

So one of the things that we've been doing is teaching integrating ethics into technical curriculum. So my team has done about like 12 or 17 different classes across M.I.T. doing this. I'm just gonna talk you through a little bit about one of those. And so it's a class called Software Studio. And the computer science department where students learn the fundamentals of software design.

And for each assignment in this class, each technical assignment, we've worked with technical instructors to integrate ethical material. And this is an example of one such assignment. So in the course, they are learning software designed by building a toy version of Twitter, which they call fritter. Because you're figuring your time away. So they'll have this set of questions, a problem set in a traditional, you know, computer science class.

And alongside them, there will be questions about the ethical implications of the decisions they might be making in their design choices. So, for example, we'll ask a question such as if your sole goal in creating fritter were to get children addicted to the technology. What design decisions you make? Or similarly, if your sole goal were to try to make it as difficult as possible for someone who's not very tech literate.

What decisions you would you make? Or if your sole goal was to prevent the spread of disinformation on the platform, what would you make? So the idea here is to sort of use these kind of creative and hopefully somewhat fun prompts to get the students to try to investigate how the decisions that they're already making. You're making these decisions in building a platform. How are the decisions that you're already making valuate? How do they have ethical implications?

And so this is just sort of one of the concrete steps that is sort of core to things such as value sensitive design is revealing. Where sort of what might look like value neutral or purely engineering decisions. In fact, have ethical consequences. And that's a skill. Figuring out exactly what those things are when you're setting down to build a technology. OK. So, again, that that's just one example of the kinds of things we're doing. Happy to and keen to talk more about that in the Q&A.

One thing I alluded to advertise at the beginning was that this sort of approach to learning objectives that we're advocating for and know by by no means new to us so that we sort of have our own spin on it. But in any case, it's the approach we're advocating for. Does.

It does break somewhat from a kind of traditional approach to engineering, ethics, pedagogy and that traditional approach which you'll find in kind of engineering textbooks, engineering ethics textbooks, and also in the sort of more and more current movement to get ethics into computer science classrooms is based in moral theory, basically, like we teach students kind of bite sized versions of what they might learn in a philosophy classroom.

We teach them philosophical distinctions, hopefully philosophical distinctions that have some connexion to some technological issue. But at roots out of the idea is to teach a bit of philosophy. We think that philosophy has a place for sure in ethics, engineering and vocation, but it shouldn't be the focus. That's the idea. Anyway. The reason is that philosophy is not, nor is it usually meant to be applicable in practise.

The kinds of questions that philosophers find interesting, even in ethics, are often not ones that are going to be the kind of thing that a computer science student is going to be wrestling with, you know, on day one of their their new job working for a tech company. So that's one issue is that the the the topics that philosophers are concerned with are often sort of not directly relatable to what the engineer is doing in practise.

The second issue is, even when they are, you're still dealing in the realm of theory. So the student might learn a little bit of theory that relates to what they're thinking about. But how do they apply that to practise? Let's do that work for them. Let's teach them directly what they do in practise and have them build that skill. OK. And I say this as a philosopher. I'm a philosopher by training. This is no knock on philosophy.

This is just trying to think of what is the best way to reach that outcome, where students are able to engineer ethically. OK. Now, all that said, there is certainly a place and a necessary role for kind of complementary ways of thinking from the humanities and social sciences.

When we think about exercising ethics as a skill, one thing you learn in a philosophy classroom when you're studying ethics or political or social philosophy is a certain kind of moral reasoning or a certain kind of awareness of it, of moral facts that you might not appreciate otherwise. That's important. Those things make you better. When we're thinking about how good you are as practising ethics as a skill.

Similarly, understanding the sort of historical lineage of a certain kind of technology can belt. You better understand how that technology fits within a socio technical system. We don't engineer in vacuums. Students need to understand how what they're doing operates within a broader system. And there's plenty of work in the humanities and social sciences that will help them understand that.

So the approach here is one sort of where at the core we teach methods of responsible engineering and kind of complementing that and interconnecting with that. Are these methods and insights from the humanities and social sciences? And of course, there are technical elements to this, too. But in some ways, that that goes without saying. All right. So that is the means to reach what we're thinking of. This first objective is to get students to be able to engineer ethically.

The second objective is, remember, for them to be ready and willing to do so, for them to be motivated to do so. How do you go about doing that? This, I think, is a really difficult question and kind of in some ways a matter of, you know, really sort of like behavioural psychology. But there are some things that we think we can do that push us at least in this direction.

And that kind of the keywords or the catch phrases we're thinking of are to help students develop an interest in and proximity to ethics. So for students to think ethics relate to what I do as a computer scientist, to what I do as somebody who's making it technology, not to what's just happening in the news.

Like, that's not my problem. And for them to not only feel like it's proximal, but like they care about it, like, yeah, this is something related to what I do and I want to be involved in it. It's not just not my problem type thing. OK. So how do we do that? Well, one of the things we've already seen a bit as I mentioned, a lot of the work is integrating ethics curriculum into computer science classes.

That relates directly to the technical material that the students are working on, that develops a sense of proximity. If you're seeing in your own homework, as you're learning to be an engineer, how the engineering decisions you're making have ethical consequences, your seeing the proximity between engineering and ethics. So there's sort of integrating or embedding approach which goes back 30 years at the University of 20 in particular.

This is something that's been going on for a long time is, I think, particularly well suited to developing this sense of proximity. Here's another thing that we can maybe do to engender the sense of proximity is class. My colleagues and I developed there's a there's a link to the syllabus. There are a class that we developed and piloted last this past summer with 70 students at RMIT. And the primary learning objective of the course was just to get students to be more interested in ethics.

And the idea is that we would try to do so by allowing them to come to ethics on their own terms and by way of their own experiences. So here's what that means, that the class had no lecture rather than the students were would meet in groups of about five with a graduate facilitator each week. They would talk about some topic that that we assign. We would have readings and short assignments. But the sort of the trajectory of the discussion would be very much driven by them.

What did they want to talk about? What were they interested? So there's no one standing at a front of the class saying ethics matters. You're making a technology. You're doing a bad thing. Here's all these ways that technology is bad to use a caricature, rather. We're trying to present them with ethical issues and let them explore it, to come to it on their own terms.

And then the experientially part of experiential ethics, the idea that the students will do this specifically based on their own experiences. So we connected this to the internships or undergraduate research opportunities that students were doing at M.I.T. and encourage them to reflect on those in, for example, their final projects. So one student who was working at law tax firm over the summer did a final project about racial inequality in the US tax system.

So that's experimental ethics. What's another way to try to get students interested in ethics? Well, we have to be open minded, like there are a lot of interesting things, questions in philosophy, in policy, in the humanities. Technical questions. There are all sorts of really rich and interesting questions about ethics of technology, about the social implications and history of technology. Let's just try to get students interested in those things.

So here's one example. Of course, I taught that was just a pretty straight up applied or practical ethics course in the ethics of technology. We didn't do so much of the responsible innovation. That wasn't the point. We were teaching philosophy. And this is the kind of thing that some students got really excited about it. And that's great, because we want to get students engaged in as we want to get students motivated to do sort of ethical thing to engineer responsibly.

And so we need to be creative here and not be dogmatic. Let's follow what the students are interested in and give that to them so that they develop this sense of proximity and interest. OK, so that's the big picture. Ready, willing and able. We want students to have this skill of engineering ethically and we want them to be motivated to do it. This is a hard thing to do, though. And so we're going to close a little bit and talk about some obstacles.

So it's no surprise, really genuine change in students is readiness. Willingness and ability requires dedication to ethics curriculum. You don't change the way somebody is motivated to do something overnight, especially when that thing is often going to sort of potentially come at some personal or professional cost. Once they're sort of maybe advocating for an ethical outcome in the workplace, that takes time.

So does developing a skill. We spend a whole undergraduate career teaching someone how to be an engineer. The skill of engineering takes time to learn. So, too, does the skill of ethical engineering. It doesn't come overnight. And so that need for sort of significant dedication raises obstacles. For one, it means that there'll be less technical material in the curriculum. There are only so many hours in the day. There are only so many hours in education.

And if you're teaching students more ethics, then something else has to go. And in many cases, that will be the technical material. So there's a tension there. There's also sort of the institutional obstacles, which are that you need people, you need sort of a workforce to teach ethical engineering practises and insights from the humanities and social sciences. That's the kind of thing that needs to be funded. These people need to be able to work with the technical instructors.

It's a non-trivial endeavour, especially when you're trying to do it at a scale where we're really going to change how the students are ready, willing and able to engineer responsibly. All right. Thank you. Peter, I think you're muted. Thank you very much, Miles. That was extremely interesting. Could I just try pushing back a little bit against some of what you said there? So you've got the idea of teaching ethics as a skill.

And I'm one of the things you're doing there is encouraging students to produce this Frita toy version of Twitter, and you can give them different aims there. Now, I can imagine that some students might think. Wow. Getting the users to be addicted to it. Isn't that cool? Or producing something that will make misinformation go viral. Wow. How exciting. Because I think, you know, a lot of the people who who end up writing computer viruses, part of the appeal there is to write something cool.

It's going to just go around the place and they don't think very much about the ethical implications. Now, I can imagine somebody saying that if you focus on ethics as a skill in the way you've described and neglect the classic moral philosophy. The risk is you're not actually giving them a reason to care about being moral. So how would you respond to that? Yeah, certainly. I think that's exactly right. I mean, if you've got students in, these things are a double edged sword.

The technology just sort of showing them what the technology can do doesn't push them in one direction or another. But I do think that it's sort of like a core idea in terms of getting them to see what design decisions will result in which outcomes heads off this problem that we often talk about, which is like an unforeseen or unintended consequence. People are building something and they don't understand what ethical implications

they might have because they don't even understand what might happen. They're not sort of thinking creatively about that. So sort of a couple of points to sort of build on that. One is that we don't just try to get them to think about, like, how would you abuse the system in this way? We also sort of give them some tools to think through well, what we call sort of moral lenses of think through a different sort of ethical aspects of the possible effects that they're going to be having.

And we don't think, for the most part, that you have to do moral theory to get people on to what are different sort of salient ethical aspects. There is often, in my experience at M.I.T., at least, a very strong sort of like consequentialist mindset and computer science students. Mm hmm. So there's a ways to sort of disabuse them of that, which is not to say the consequences don't matter without sort of, you know, teaching them all three formulations of the categorical imperative.

They're sort of different ways to do that, that don't get into the theory. And then there's the other question you raise, which is just how do you teach them to be moral? So, OK, now they're really attuned to these ways in which they can sort of addict their users or do bad things and they're going to make more money by doing that. Well, that's where the sort of the willingness and motivation comes in.

And they is it not is it not plausible that some of that willingness and innovation could come from discussing issues like UNIVERSALISE ability? Miten, your interests are as important as mine from an objective point of view of virtue, ethics or moral psychology. In other words, doing some more traditional moral philosophy might actually help to motivate them.

I think it absolutely can. And so this is the sort of this idea that I was trying to articulate at the end, which is that when we think especially about the mode, we can really separate the skill building point where moral philosophy, I think, has a much smaller role, because a lot of these are like looking at a technology and understanding the space of alternatives. Who are your stakeholders? What are the decisions that I'm making? So that's more on the skills side.

But on the motivation side, we have to be flexible. We have to figure out what works and it might be different in different places. So some students might get really excited about the moral theory and that's their way in. And that's fantastic. And other students might get excited by the history of it or, you know, understanding, you know, what the corporate culture at Google is like. We have to sort of be flexible in that way. And I do think that moral theory is a great way.

Like this ethics of technology class. I was a practical applied ethics class. Students got more excited. The engineering students at M.I.T. got more excited, like express this about ethical engineering in their own work. Even though we weren't teaching responsible engineering practise, we take big losses. That's interesting. That's it. So bringing in the ethics as a skill could quite independently of another value, actually be useful in motivating an interest in it.

I think so, yeah. Yeah. Yeah, because I imagine there is a tendency for computer science in general to think of ethics classes as rather orthogonal to their main interests. Well, thank you very much indeed, Molly. That's that's fascinating. Let's now hear from Helena. Helena Webb is senior researcher in the human centred computing theme at the Department of Computer Science.

Research has been highly interdisciplinary, involving a range of projects to investigate the impacts of computing on social life and the lived experience of technology. Together with Max. Max Van Cleek, who will be speaking next. Helena developed and delivered a new compulsory course for undergraduates in the Department of Computer Science on ethics and responsible innovation. And the course ran for the first time last year. It's currently being delivered for the second time.

As part of a work in the human cancer, human centred computing theme, Helena also contributes to a number of wider education initiatives, which she'll talk about in her presentation. So, Helena, we'll be really interested to hear what you've learnt from this year and something of a teaching ethics in Oxford. Two competing students and your other outreach initiatives over the year. Thank you very much. It's really great to have the opportunity to be part of this discussion today.

And I just share my screen. Hopefully that's come up. Okay. Fantastic. Yes. So, as Peter mentioned, I'm a senior researcher and I work in the Department of Computer Science as part of a group, a research team called Human Centred Computing. So we are an interdisciplinary group of researchers and we carry out projects that examine the impacts that contemporary computing systems have on individuals,

communities and societies. And our work tries to identify ways that these innovations can be ethical and better support human flourishing. So ethics is central to a lot of what we do, often with a very applied focus. And this focus often involves an interest in education in both formal ways and non formal ways.

So in this presentation, I'm going to talk about two things. So, first of all, I'm going to talk about some of the education initiatives that I've been involved in as part of our work in the human centred computing theme. And in particular, that takes place as part of projects that we carry out that are underpinned by the initiative known as Responsible Innovation,

the initiative that mine was already mentioned in his talk previously. And then I'm going to talk about the undergraduate module, that maxim I developed on ethics and responsible innovation that we delivered for the first time last year here in the Department of Computer Science at Oxford. So most of the projects that I'm involved in as part of the human centred computing theme are underpinned by the initiative known as responsible innovation.

And this is an initiative that has developed a great deal of traction in academia, industry and policy in the UK and the EU and more broadly in the last 10, 15 years or so. And the idea of responsible innovation is to bring together actors across society to work together during the entire research and innovation process.

So you bring together researchers, scientists, citizens, policymakers, businesses, third sector organisations and so on, bring them together so they can engage with each other and talk about innovation. Talk about the potential consequences, both positive and negative that it might have, and identify ways to address those consequences early in the lifecycle of the innovation process.

So it's about finding out ways to try to mitigate the potential negative consequences of an innovation before it is implemented. So the idea is that if you bring together these stakeholders to work together, you can produce better outcomes. You can ensure that innovations meet the values of society, meet societal needs and interests. So this responsible innovation perspective really emphasises stakeholder engagement and the sharing of understandings.

So it's a form of ethics being carried out in very practical ways, and it naturally brings in some educational elements as well. So education to focus on good practise and innovation or ethics and innovation. So in our group, we carry out a number of activities that come under the umbrella of education in a formal and informal ways. And that might focus specifically on ethical a I or might focus more broadly on icy tea and digital technologies.

So I'm just going to mention a few of the activities that fall under this umbrella of education and in different forms. So the first of these is Orbit, which is an organisation that is an observatory for responsible innovation in ice tea. And this observatory provides I'm training consultancy and research. So at the moment, Orbit is involved in the training of doctoral students across the UK.

And this is seen as something that's highly important. So we have these doctoral students who are just starting out on postdoc postgraduate work on various issues around science and technology and innovation. And here we have an opportunity to embed in them as they begin that training understandings of ethics and practise of responsibility and to think about how they'll carry out in their own work.

And then they can take it forward as they go through their careers. If it's into further research, it's in science and industry and so on. So it's a valuable opportunity to embed ethical understandings right at the start of their journey. Another initiative that I was involved in concerned one particular project, and this was the unbias project. So this was a project that looked at algorithms.

So controversies around potential bias and algorithmic systems, questions of fairness and algorithmic systems. So in this project, what are the outcomes that we produced? Was a fairness toolkit. And this tool kit has various resources that are available for stakeholders of all kinds to help them to reflect on issues about. About bias and algorithms, about fairness and algorithmic systems. And what we can do to promote and foster fairness in these systems.

So one of the elements of the toolkit is shown on this slide here. And this is a set of awareness cards. So the cards have different exercises and different pieces of information on them. So they have exercises to take you through the processes of designing an algorithm, deciding what pieces of information go into an algorithm, or they have exercises to help people consider.

What kind of values might we want to be embedded into an algorithmic system or what kind of values would we want that system to uphold? These toolkits are available as physical and digital copies for a wide number of stakeholders, and we've sent them out. And they've been used with students, with professionals and all kind of areas. So design, science, law and so on.

And there are really great opportunity for groups to engage together in discussion and foster this kind of critical thinking about algorithmic systems and also help people to feel empowered to make better informed choices about decisions about how they interact with algorithmic systems. So this is an eye. This is an example of some kind of all informal educational wide engagement and educational opportunity.

And other activities that we've carried out across a number of our projects is the ethical hackathon. So this is a slight twist on the traditional idea of the hackathon. And computer scientists and engineering students are very familiar with the hackers on model in which you compete together in small teams to face some kind of design challenge. It's a fun competition. It's a chance to exercise various skills and so on. In an ethical hackathon, we make our teams interdisciplinary.

So we bring together teams of computer scientists, engineers, social scientists, philosophers, lawyers, and they all work together. And the idea is that they can share expertise and share experiences amongst each other. We also make the design challenge something that's less focus on the technical, more thinking about ethical issues.

So, for instance, in some of the ethical hack zones that we've run, we've set students are challenged to say that you're developing a new social network and you're committed to the responsible use of algorithms. How are you going to do that? So, first of all, the team would need to work together to set out the ethos that their network would have. And then they need to think about practical steps to put that ethos into action.

So it's a challenge for them to think about, to identify, first of all, these ethical and responsibility issues and then to try to translate them into practical steps. So the students learn from each other from the different skill sets that they have. And we also incorporate some formal teaching sessions into the hackathon as well. So they have that more formal teaching element, too. And as Milo was saying in some of the work that he's been doing, the emphasis is on creativity and fun.

So these hacker forms designed to be fun encounters. Students get to learn from each other. They enjoy spending time with each other. They present on their ideas and their prises available for the best ones. At the end of the event as well. So those are some examples of some of the wide educational initiatives that we've been involved in as part of the human centred computing theme.

Now I'll talk specifically about the module that Max and I developed here for, here for the computer science students in our department. So in our department, in computer science at Oxford, ethics hasn't traditionally been taught as a core subject. And sometimes it's difficult for our students to see it as an issue that's relevant to their own studies, because a lot of the teaching that goes on in computer science here is quite formal.

It's quite mathematical. So they don't necessarily see it as something that's relevant to what they're doing. However, after a period of time spent talking to the department, we had the opportunity to run a course and this was put on for the first time last year and made compulsory for our first year undergraduates.

The aim of the course was to introduce the students to core ethical principles and normative theories and to deepen the understanding of ethical challenges in contemporary computer science. So we did spend time talking to them about moral philosophy and different normative theories. And we also talked about certain contemporary issues around A.I., around data surveillance and so on. We introduced ideas of responsible innovation and other practise based approaches.

So we talked them about value sensitive design or about corporate social responsibility and other kinds of practical approaches to what we wanted to do was introduce these conceptual ideas, but also very strongly encourage critical reflection from the students as well to get them to think about how they understood these issues and how they could see them as relevant to their own,

their own work and their own activities. So we run the course as a series of lectures followed up by seminar sessions and seminar sessions. The students work together in small groups. These are obviously from last year when it was possible for them to be close to each other. So the students work together in small groups and we gave them tasks that required them to draw on all the elements that we've covered in the calls and try to apply them practically.

So, for instance, we might give them a task where they had to imagine that they were the head of an organisation and that organisation was bringing in an A.I. tool for recruitment that could, you know, work through CVD or decide who might be employed for that role and so on. So that task might be to consider how can that organisation make sure that they use that a system ethically?

What challenges might they face? What kind of steps do they put in place to make sure that there's no bias in the system, that humans in H.R. aren't worried about losing their jobs and so on? So they seminars themselves had a very kind of practical focus thinking about translating these more conceptual ideas into practises and really challenging the students to reflect on their own understanding of these issues and how they themselves sort of see them as being put into practise.

And this includes encouraging to see conflicts where they might arise. So the ways in which sort of ethical concerns and ideas about upholding values can sometimes come into conflicts with the economic imperative and the profit motive. So we worked a lot on identifying those kind of tensions and those challenges and talking about different kinds of ways, different solutions you might find to those challenges.

So I know that Max is going to talk a bit further about the course and some of the challenges that arise in teaching these issues to students, so I won't say much more about it. But just to finish, I'm going to quickly talk about the three key issues that Peter was saying we want to cover in this discussion. So the first of those issues was what are our learning objectives if we're teaching ethics?

And I would agree with Miloje in the importance of a very grounded approach to it, one that taps into the experiences of those that you were talking to as being a really helpful way in getting people to understand these kind of issues. The other question was, what are the suitable means to meet those objectives? And I think there are many different means that we can use so they can be formal teaching methods in classroom situations where they can be very informal as well.

They can be incorporated into other kinds of activities, and you can be teaching ethics. Even if the people that you're teaching aren't students, they might be stakeholders. You're engaging in other kinds of activities. And then the final question that we were given concern, the obstacles and I know the discussion will focus on this a great deal because there are a wide number of obstacles to truly being successful in this kind of teaching.

So I'll just very briefly offer a few here. I think one obstacle, one challenge that we've already mentioned is showing the relevance to people. Why should I care about this thing? Why, you know, call the ethicist. Just do it. And I don't need to worry about it. I can focus on my computing or my engineering and other challenges, avoiding ethics, becoming just a tick box approach that's just done and kind of siloed off from the rest of the curriculum.

And then a further challenge is bringing in an interdisciplinary perspective, because I think there is a great deal of ways in which ethics and social sciences and law etc can combine in this kind of teaching. But genuinely bringing in the interdisciplinarity can be quite difficult to achieve. So those are the challenges that I'm going to offer here and look forward to discussing later on as well. Thank you very much, Helena. That was that was very interesting indeed.

I'm just pointing up a contrast of your approach with Milosz. You did bring a traditional ethical theory. And so you were telling them about deontological views and consequentialist and virtue, ethics and so on. Did you find the students engaged with that or did they turn off? I think they did. I would say that they did engage with it.

And I think what's very helpful is actually to use those theories and say, you know, you use these positions all of the time, saying if you think about when you have discussing with your friends about, oh, should we have driverless cars or, you know, what's the solution to the trolley problem and all of that kind of stuff, the arguments that they make, they naturally pull on these consequentialist and dancehall positions without people necessarily realising it.

And I think I see if you can make that point. It can be a very useful way of showing people in ethics isn't just this sort of philosophical thing that that people do in a room and that they're striking that there's actually ethics is something that we engage with all of the time. So I think it can make a very useful teaching point to to bring in those perspectives. Yeah. Okay. So you. I mean, Descartes would like this.

You start from the particular and moved to the general and you say, look, here you are thinking in this way. And if you if you think through the implications of it, it's going to lead you in, as it were, an ethical direct or the direction of things that have underlined being seen as underlying ethics. One other question. I mean, this is obviously very new. I mean, ethics is coming in across the world in a teaching in a way that it previously hasn't been.

Are there any outcomes of this that you've not expected where you've tried things and. You know, being rather surprised by the outcome. Oh, I think that one of the things that I find very rewarding about this kind of teaching in general is you can't always predict the things that people will take away from it. So for our course, we are students to write up some reflections at the end of the course and write about how they might use these ideas going forward.

And some of the things that they wrote were really, really rewarding because they talked about kind of other activities, you know, extra curricular activities that they're involved in and how they're going to use those ideas in that work going forward. And that wasn't necessarily one of our core teaching objectives, but it was really fantastic to see them seeing the relevance of that and taking it forwards.

And similarly, we find with the sort of the broader even the non formal teaching that we're engaged in, it's often used in ways that we don't anticipate. So with our unbias cards, they've been used by a much broader range of people than we first anticipated, and that's led us actually to develop extensions to them. So so my colleagues who worked on the project have developed further materials for facilitators using those cards.

And also, I mean, I decision makers tool kit, which has just come out as well, which I was supposed to mention. And when I had a slide up with the with you were all for the projects. So I'll just mention it now. And if anybody would like more information about it, they can get in contact with me and I'll pass it on. But I think that's a fantastic thing about when you're doing these engagement and education things is the kind of the abduction.

You know, what people take away from it isn't necessarily what you set out when your core objectives. It can be something something else as well. And it's because, you know, people are seeing it through the lens of their own experiences and their own interest. So it can have many more positive benefits. And you initially set out for them to have.

Well, thank you very much. Is really interesting. And now we're onto your collaborator, Max Van Cake is associate professor of Human Computer Interaction that the Department of Computer Science and a fellow of Kellogg College in Oxford. He'd like. He has a background in M.I.T. and while studying at my team, is a research assistant at the M.I.T. A.I. Lab, the media lab and the computer science.

They are my lab. So. So he ended up studying under some of his most influential founding fathers, including Rodney Brooks and Marvin Minsky. But enviable, Max, since moving to the U.K. first at Southhampton and then it's OK that Max has worked closely with Sir Nigel Shadbolt, who chairs the steering committee for the New Ethics in a eye institute. So. So Nigel has really led that charge and played a huge role in getting the new institute started.

Since last year, as we've heard, Max has been teaching ethics and responsible innovation. Along with Helena, and they developed that course together from scratch. And you're going to focus? Oh, actually, I've just noted a rather whimsical thing at M.I.T., Max. I understand you used to be an electronic music deejay with a DOT pop radio show. I had robot music designed to soothe lonely robots. Is that true? Yes. I spent so much time in robot labs that I deserve.

I believe that they deserve, you know, acoustic accompaniment. So I ended up becoming an electronic music deejay with IBM, VR, M.I.T. Radio. Excellent. Right. Well, over to you. OK. Thank you so much, Peter. And yes, thank you as well to the Institute. Ever since the institute was founded, I was excited to get involved. And there were some excellent seminars last term about ASX. And the reason that I am so keen to be to give this talk here is twofold.

First of all, you know, this is a very this is an area which is fast moving and extra incredibly important as the pace of A.I. and technology has continued to accelerate the pace at which new ethical problems and concerns are are arising as also correspondingly seeming to accelerate. And so that is a major reason for it.

The reason why I think it's exciting, interesting, exciting to talk about education is that in response to these emerging issues, there have been a huge number of different courses and curriculum curricula that have been proposed across the world. A lot of my collaborators in the US, for example, have put their syllabi online.

And I think it's very important now to start conversations about how best to go about this endeavour to try to, you know, to try to understand, to teach ethics to to integrate it throughout various engineering and computer science curricula. And that's what I'd like to talk a little bit about today. So I was asked by Helena to to take part in the design of a new module, as you've just heard.

And I'm going to talk a little bit more about about that here, that name of my talk as victims of algorithmic violence. Now, this is meant to reflect the fact that sooner or later, because of the fact that algorithms are now going everywhere, you or someone that you care about, it's going to be a victim of some sense of algorithmic violence. And we'll talk more about that later. The let's see if my slides can work.

There we go. So the brief map of this talk, I'll first talk about a background about of the course and why I was so interested in teaching it. I'll briefly describe fill in the gaps, a little bit of the details of what Helena and I jointly decided would make an appropriate first course. Again, this we were very much alpha testing this course and we're very keen to iterate it and make it better over time. And what we want. I want to do is go over a brief overview of our objectives there.

And then I want to talk for a moment about reflection. So how well, it it works. Challenges that we encountered and student reactions and then and on talking about. So what now we know where can we go next? So I usually begin my my talks with a brief contact warning, we will be mentioning a number of of spicy issues here. If any of these things are of topics that are sensitive to you, then you may consider not tuning in for the next 10 minutes while I talk about them.

But anyway. Yes. So let's first talk about why I got so interested in trying to teach this class in joining Helena on this mission. The reason it's not only because I help lead a group that is looking at the design of ethically technology, but because it seems that every week we have new, egregious examples of ways that technical systems are doing, you know, unbelievably terrible things.

And, you know, just to mention an example from last week, here is the example of the Twitter image cropping algorithm and the they just explain what this what this problem was, was the tweet. Twitter is, as you know, the world's most popular sort of microblogging platform. And it has this problem that tweets are this big. So they're quite small. And so if you upload images to share and tweet, it has to figure out how to align the images so that you can see the bits.

It's that it can get sort of a bit of it visible. And then you can click on it to see the rest. So somebody discovered a researcher discovered last week that if you upload photos of people and one person, for example, Mitch McConnell and then other person, Barack Obama, and you you have you create the images artificially to be long and narrow so that the image copying algorithm has to then try to figure out a crop. And you permit them. What do you think the image cropping algorithm would do?

Well, a very sensible thing would be to just take the middle, for example. And you would you end up with is an image that's just white or blank for both of them. But what the algorithm was found to do, in fact, was always choose Mitch McConnell instead of Barack Obama. Now, the reason why it was later sort of explained by Twitter was that they were trying to figure out how to make cropped the image to maximise salience.

And the data set that they were using to do that one. What had more faces of white Caucasian nature than of black or darker skinned people? And this is, you know, just one of the many examples. We also have like plenty of other examples of devices that work less well for for those who are darker skinned or who are not white because of these same kind of unbalances imbalances in the data sets that people are using.

Now, these are these are terrible examples, because what happens is that it ah, it exacerbates makes worse. And then in some cases, quite literally promotes the erasure of marginalised groups, members, marginalised groups. And and, you know, and these are not very complicated to demonstrate that these seemingly obvious kinds of things.

But this is just the tip of the iceberg. We have a huge number of ethnic ecosystems on a daily basis that we talk about, which, for example, do everything from target advertisers beats to teenagers who are particularly depressed or have low body confidence systems that are optimised to to spread conspiracy theories to those most likely to believe them, systems that are designed and implemented based on a history of racist pseudoscience and so on.

And so this was, you know, this did it require. It didn't require much to persuade me that this is a very important topic. And and what was really exciting was that we were given the opportunity by the department to create a mandatory module for computer scientists. Just a little bit of background for our depart about our department. So our undergraduate programme in computer science is currently ranked top of the world by subject and one of the rankings.

You know these rankings. This one's the times. Higher education when it's. But, you know, overall, it's just a very competitive, highly competitive programme with a very strong theoretical emphasis. But what's interesting here is that it's not just computer science. We have three different courses and one of them is actually a joint programme, computer science and philosophy, and another one is maths and computer science.

But regardless of which of these streams you're on, you are required to take our new module. So this set up a very exciting opportunity for Helena and myself to try to interest students, get it, get students interested and excited about it, about ethical challenges and and to give them a means by which they can think of them. So our main goal was to prepare students to be able to, one, identify and to start to think about an address.

Real world ethical challenges that are articulated by digital systems and in order to do that. This is you know, this is Oxford and we're not we're not afraid of embracing our tradition. We wanted to equip students with the foundational conceptual frameworks for thinking about ethical positions. And for that, you know, we did not hold back on ancient philosophy, as you know, in order to establish this foundation.

And then we wanted to relate those who want to connect us to contemporary ethical challenges. And then finally to apply them to situations, as Helena just described. And in order to do that, we were able to draw from a huge variety of of text texts that have been recently written on ethical challenges. And we'll revisit those in a bit. But weapons of mass destruction, invisible women and Benjamin Benjamin's race after technology,

where three of the books that we found extremely useful. Now, this slide might get me in quite a bit of trouble. However, one thing we really wanted to avoid doing was. Yeah, you must understand. So. So just going back for a second, we only had four lectures and two practical. So it's very compressed. The problem is undergraduates here are very stressed out and they are required to take a lot of classes their first year.

And so adding unbranded Tery module in their schedule is not an easy thing to do. And so we had to really, really figure out a way that we could compress this material in a way that was still meaningful and engaging. So something had to go. And the thing that we decided had to go, were they the most common kinds of of tropes associated with A.I. ethical challenges.

Now, what's particularly funny is that these three challenges are very championed or or in fact, they come from oxfords that the various philosophers and Professor Nick Bustards superintelligence book, for example, describes the superintelligent hypothesis, which states that at some point I might become out of control and vastly more capable than humans and thus will not need to keep us around.

And the of course, the trolley problem is perhaps the most well-known ethical conundrum or problem in in history, which states the question of we need to be able to solve the question of whether what the value of a human life or several humans lives are in order to determine whether, you know, in order to determine how a system should behave.

So if a trolley were out of control and it needed to either sacrifice one person or or several people and they had different properties and histories, which would it choose and how can you make such a decision?

And finally, the third thing that we didn't talk about more than more than just describing the problem are the is, of course, the issue of robot rights and information ethics, which Professor Florida and others have talked about whether information itself should exert a moral claim and and thus whether technical systems comprised of information themselves should exert moral claims.

The interesting the reason why we didn't talk about these is that they don't currently present pressing issues to worry about. And in some cases, many of us in computer science department don't believe they ever will present issues worth worrying about. Sorry. Worth is a strong word. You know, that that is that is relevant to actual problems. They are very interesting questions to discuss.

If you'd like to know more about why I don't believe the superintelligence hypothesis will ever occur in reality. Please see these scholars who are who can articulate them. The reasons vastly better than I can. As far as the the trolley problem is concerned, we discussed it briefly. I think one of the most mis applied versions of the problem is in terms in the context of autonomous vehicles.

And and so you've probably heard that in order to create autonomous vehicles, we're going to need to solve the trolley problem so that the car can decide whether or not to kill a person X or maim some other person Y. In fact, those who are designing safety critical systems and autonomous vehicles here and elsewhere will tell you that the trolley problem is definitely not anything close to anything they worry about,

because as soon as you injure or hurt somebody, the system is outside the scope that it has. It has failed. Right. You design systems not to kill people by any means possible. And as soon as you're in that space, then you're in a sort of an erikka failure condition and you need to cope with that in a particular way. On the other hand, there are some really compelling and also tragic applications of the trolley problem, which we did discuss.

And one of them is very relevant right now with the pandemic critical resource allocation and medical ethics to say say that you've got a finite number of ICU beds and you have more people who need them. How do you then provision and assign patients to beds or then again as well to disaster relief? Hopefully we won't have another scenario in 2020, at least related to that. So what kinds of problems did we focus on? Well, we focussed on it.

I wanted to give you a very high level overview because obviously I am limited in time here. But one, you know, of course, a primary example is what Helen alluded to in terms of algorithmic bias. So it is you know, it is a fact that voice recognition systems today work better for our have higher accuracy for men than for women. And what's really terrible about this? Is that this can get cascaded on.

If you have systems that rely on speech recognition as the first stage of their pipeline, for example, they kind of a hiring systems that Helena alluded to there require essentially an understanding of what a person is saying in order to make a decision whether somebody is a good candidate will then automatically have higher error rates for women.

And this is particularly the problem because, you know, women are already facing, you know, great challenges, greater challenges than men and getting, you know, through, you know, glass ceilings and in order and getting hired, especially in particular sectors. And, you know, this kind of thing only makes matters worse. And it's particular. And if you then look at groups that have accents or, you know, any other variation in speech, then the recognition rates become significantly worse.

And you think about how those variations then correlate with marginalised groups. Already these systems are only going to make things worse. So we talked specifically about things like that, about accuracy related to the groups. A fundamentally different kind of harm that we also described were representational harms which deal with with with essentially the ways that that that people, places and ideas are then associated through the data that we use.

For example, Professor Latanya Sweeney is research that revealed that African-American sounding names were more likely to be associated with ads suggesting arrest in Google very much demonstrated the sort of representational harm that these African-American names should be associated in some sense with with arrests. And and this was sort of the kind of thing that we were talking about.

I'm going to skip very quickly through some of the other other lessons, because I would like to get immediately to our discussion of reflections. So let me see. Sorry about that. I think I was slightly ambitious in terms of what I could cover in time, another sort of this ultimate thing. I'd like to talk about the front of our reflections. Was the question of whether art of technological artefacts could be unethical or ethical.

And for this question, I believe that, you know, Milo talked about value embedded design. It was very useful for us to talk about the many kinds of systems that computer science students and engineers use on a daily basis. And talk about whether we could see them as, you know, as ethically neutral general purpose or whether they are in some sense good or bad. And that was that was a very exciting exercise because it related to a lot of the kinds of activities they performed to the databases.

Okay, so now I'd like to just spend a couple minutes at the end talking about how this split. So now this this information diagram is not based on actual figures. This is based upon my sort of recollection and heuristics. I'd like to say semi random figures representing a subset of the students with particular reaction to ethics material. So there were there were there were, first of all, a significant group which were which were really sort of ready for this.

Already they were already aware and motivated to look at ethical challenges. And really, you know, this was the group that was like it was like giving water to a sponge. They were ready to to get this material and start working immediately. And we were very you know, they were the least difficult to convince, very excited. And essentially, all we were doing is providing examples which Brett, you know, broadened their initial expectations.

In general, a majority of the students were not in that group. While there were a significant number in that group. The majority of the students started, it seemed to start out with a slightly more reluctant and sceptical view. And I'll talk of in the next slide about why that might be, because in part, they started with much narrower expectations of what ethics would talk about.

And, you know, there was a bit of element of surprise when we started talking about the breadth of the kinds of issues that they discussed. And it felt a little bit like they were navigating this sort of local maxima. And we were really trying to push them to a place that they were they were able to see the large picture. But then we had the last group. Now, the last group, which is a minority, but nonetheless, I think an important minority.

Whereas a highly sceptical students, first of all, that the students that, you know, when you say you need to take this class, they are the ones that are say, do I really need to you know, again, why why is this anything related to what what I'd like to call them the turkey 20 percent. So the turkey 20 percent. Why were they so tricky? Well. One way that we dealt with the trickiness, one of, first of all, is that they saw ethics as impinging on their private, their their activities.

They saw and it's very useful. So for us to frame ourselves, as in within groups, as computer scientists, why you want a hacker on your team is that then you can actually describe you can connect to them as a as a mentor rather than an interloper rather than somebody who is introducing ethics is something that you need to do, almost as parents would tell somebody that they need to clean their room. It's much more useful to have friends.

Just shame you based upon how dirty your room is. But another reason is that it is language. So it's very important to be able to for the for this tough 20 percent speak the language. And and that can be very challenging because, you know, interdisciplinary work inherently deals with different definitions of things. And even the use of the term, for example, A.I. Imprecisely, which is notoriously done by the media, can be a dead giveaway that you really don't know.

The systems work very well. And that can put people off. They can put students off saying, OK, here are these people. They don't know what they're talking about. And they're telling me what I can do or what I can't do. But another, more positive say of effort, of being a hacker, a computer science, having computer scientist on your team is that there are some really deep and profound connexions between theoretical A.I., theoretical science and philosophy.

And what's great is that we know we have something like 15, 20 percent of our students are CSM philosophy students, and being able to make those beautiful connexions is very valuable. Next, you want to have a hacker because you need to understand the hacker mindset. So, you know, the hacker mindset originally that Mark Zuckerberg is to move fast and break things right.

No news allowed. And so what you want to do is you want to set the poise of the work so that you're not the fun police telling people what not to do. But instead, you're setting the bar higher. You're introducing a bunch of new challenges that will make your life perhaps more challenging in the short term, but will allow you to build better things in the future.

And so so that is is why. So that's there's a sort of the feelings that we got and the reasons that we were able to pivot with our multidisciplinary team between the more computer science angle and more of a philosophy and social science angle. I'm not sure how much time I have. Peter, do I have a one or two minutes left? I'm sorry you're needed at the moment, but if you could bring things to a reasonably quick conclusion, it would be nice to have a quarter.

Now, if a general question sorry, I'm finished within three, four more minutes. Yes, absolutely. That's more than enough time. So so an example of the kinds of sort of interesting and rich and slightly tense discussions that we had amongst students, which meant which made me realise that, you know, we're different. Our jobs are definitely not done. And there's a lot of opportunity here to improve things. Is are the following.

I give you three examples. The first one is that we were in the middle of discussing a paper which was called Discriminating Tastes, which is about Uber customer ratings and the ways that, for example, women and minority drivers get, on average, lower Uber Lyft ratings than than white men.

This is in general perceived or thought to be the case for a lot of gig economy workers and is, of course, you know, a problem because then it feeds into the algorithm, which then changes the opportunities that are given to the gig workers.

As a consequence. So it has this vicious feedback cycle. So we were discussing this case and, you know, we had a student say, well, you know, how do you know that this is because that people are being biased and that women or minorities are just not worse drivers than men. And the nice thing about this class is that we were very open to having it as a seminar and we had somebody immediately respond. Well, do you know that women have better access records so they have fewer accidents than men?

So the evidence would suggest the opposite, that, in fact, maybe women were better drivers in general. And then we had another student respond that, you know, these are good paraphrases. They're not direct quote, we do this to respond. That would so maybe, you know, they just drive more slowly or or are just too careful or aren't as nice to passengers or something, really.

And what I wanted to highlight here is that this is a real challenge because because they're trying to statistically explain the possibility that there may have been another reason besides discrimination that led these groups to have lower ratings. You tried to slice away the possibility that infinite possibilities, that there may be yet another reason that maybe it wasn't racism, maybe it wasn't discrimination. And this is a trap. Right? This is a challenge.

And and I think that what are the ways that we can get around this is really by saying, well, you know, the personal experience of people who are a member of these groups will be able to tell you and amplify that. In fact, you know, when the kinds of ways that they get treated and the ways that they get rated, it suggests discrimination rather than one of these, you know, increasingly unlikely other hypotheses.

And that kind of discussion is is really interesting and fruitful and and will allow these things to pan out. You know, again, so we want to not erase discrimination statistically. And so it's and it's often difficult to train this argument in two other very quick examples. And this one kept coming up over and over again in an a hiring scenario was the diversity quality phalluses that goes something like this.

We have to consider diversity, but that'll force us to sacrifice the quality of our candidates. When you screen candidates, you generate an N best list. And and, you know, and if you want to and if those top candidates are all of, for example, white men, then you're going to have to keep going down the list until you find a candidate that meets your diversity criteria. And we found several students hit you giving this argument over and over again.

And the point that broke that was very exciting was when we had students respond to this very strongly, strongly and say, for example, what are you measuring anyway? What is the ranking supposed to represent? You can't measure the value of diversity to a team and then another student would cave. Another really exciting point, which what was the law of diminishing returns above a certain threshold?

She said rank differences are mostly likely, likely be caused by noise and other things like historical injustices and structural inequalities. And so, you know, that kind of being able to for this students to counter that argument, I think was extremely exciting. The owner, the third and final one that I wanted to mention. What's that? So engineers will be engineers no matter what you do to them.

And they'll try to they believe genuinely that you can fix it if it's broken, you know, whereas we you know, we strongly believe and we tried to highlight that in some cases that some systems are so deeply problematic fundamentally by their principles that they should never have been built to begin with. And and so we gave a number of examples of this deep nude skin with the skin whitening app, sexual assault simulator.

And I think one of the most interesting things are examples which draw on historical racist pseudoscience, for example. There's a huge number of papers that are being produced by machine learning researchers right now to try to infer traits such as IQ, criminality or sexual orientation from their faces. Of course, this is rooted in physiognomy, which is a longstanding racist tradition of trying to identify whether people might be criminals by how they look.

And without that context, though, students may be prone to try to throw deep neural networks at everything. And what really the aha moment was where students realised that, in fact, maybe the features that these these DPL networks were were triggering on where the features that that, you know, that society has forced people to to fit was essentially they were overfitting on on features that we really shouldn't be using for determining these ever determining characteristics like these.

I just want to end by saying there's a lovely GitHub by David Daou, which is a compilation of things that should never be built. And in conclusion, the first year of ethics in response to innovation showed us a bit about how C.S. students think about ethics. But there's a lot of work to be done. You know, we have there's there's no singular text book that's being used. We were able to draw together a lot of materials from different lot of different contemporary sources that are very good.

But we sort of had to bring together bits and pieces of each. And I think the next thing we need to do is really understand how to to to help students through some of these tough traps that they might encounter in the process. Thanks very much. Thanks very much, Max. Really interesting. Right. Well, are all our speakers now available? One. What I like to do is kick off with a question that's been asked. And it's prompted partly by what you've just said, Max.

The constraints of the undercurrent graduate curriculum and how difficult it is to fit ethics teaching into it. The question ideally, what kinds of ethics, pedagogy and how much of it should engineering or a computer science student encounter during their undergraduate education? And another question made this point. Another question that made this point that actually a lot of these issues will arise with engineering students as well as computer science or A.I. students and.

Are there specific issues? This is from Leslie. What do you think about teaching ethics to engineering students as they also have that need? And maybe you'd like to comment on how much ethics should we get be getting into those courses? And is there a distinction between engineering students and software engineering or A.I. students? Who'd like to volunteer first on that? Element of it. Yes. I'm actually a social scientist. So I studied social political sciences for undergraduate degree.

And what's interesting is that in that curriculum, we start learning ethics from day one. We learn in relation to two research methods and so on. So it always surprises me to be in a computer science department where you don't have that kind of attitude. You know, ethics is kind of embedded into everything that you do. So so the question about how much ethics and how soon I would say teach ethics from day one, teach it as a fundamental part because it could.

It is. And, you know, we see all of the examples that Max's just talked about. This is why it's so important that we're teaching ethics to our computer scientists and to our engineers right from day one and teaching it as something that is fundamental to everything that they're doing. It's not just sort of like an add on thing that you have to get through, but actually it's integrated.

And I think moving forward, what we would like to see are these ethical elements that we teach it as its own course, but also seeing it integrated into other modules or seeing it sort of students being talents to think about it in other modules and other activities that they're doing. We already have it in in our department in India, too. They have to do a practical design challenge. And now the students have to think about ethical issues.

So there is a sense of building up through the curriculum. And I think the key is to keep that going and to keep getting these issues sort of embedded into what the students are doing. And I think actually it's the stuff about A.I. and that really helps sell it to our computer science students, because I think, again, because of the kind of computer science that studied at Oxford, sometimes I can ask you always for the engineers.

You know, it's the implementation of this issue, but it actually is all of the examples that Max talked about that really I think helped open our students eyes of why we need to think about it in computer science, like why it's actually, you know, in the algorithms themselves and it's the way that they're developed and the data that they're working on and so on. It's going to give rise to a challenge, isn't it? Because it can do to science has grown so much.

And I in particular in recent years, I mean, I know I spent 20 years partly in a computer science department. And, you know, whenever the curriculum was being revised, there was lots of new stuff and there was this competition. What can you what do you have to lose from the curriculum to make space? And if most of your faculty are people who weren't really used to thinking of ethics as part of the discipline, that that's a bit of a challenge.

Yes. Yes. Absolutely. The agreement. I think solution, I guess, my lady. Yeah. I just think that that way of putting it. And what he later said also also kind of pushes into this that way of putting it is very fruitful is like what is part of the discipline? What does it what counts as a good computer scientists? And if what counts as a good computer scientist is a certain technical or mathematical aptitude. And you the other things are gonna come second and it's going to get crowded out.

You're not optimising for the problem of how to teach them to be a certain kind of, you know, ethically responsible. But if we think of what counts as a good computer scientists, at least for the purposes of our education as integrating these components, then that's just one of the things that we're trying to teach. And then it becomes like anything else, you know, we're balancing whether, you know, more or theoretical aspects of computer science versus more applied.

You do machine learning versus not. There's always going to be a Trade-Off in what content. But we do. I do think that that's like the fundamental shift to think about a good computer scientist or a good engineer more generally as not just having technical aptitude. And once we have that shift, other things can follow. Right. Thank you. Yeah. Go on, Max. Kind of just say two very quick things.

One, to also reinforce what what Helen said about it was really exciting to be able to get the students in their first year to talk about this, to establish a framing. What's interesting is that the deontological idea is really sort of supported for some a feeling that people had, you know, that there's some things that were sort of bad to do.

Like, you know, there's sort of this gut feeling that there were some things that were just bad, but they didn't know that there was a term for it and they didn't know that there was like a principal that said actually that, you know, violating someone's privacy, you know, and, you know. Right.

To like to treat somebody genuinely rather than try to use them to, you know, sell more things or more engagement to be more engaged was like, you know, a violation of something that was a fundamental right that that that people have talked about humans having. So that we were hoping by sort of capturing them at the beginning that they would then be able to have that that structure, that they were able to then apply that lens to whatever they were looking at.

But that being said, the second thing I wanted to say was that there's a lot of really domain specific applied ethics that we could go into. So the first thing we talked about, we said, have any has anybody even written a line of code before? Of course, most people had in the computer science programme. So did you decide to put it up online? Was that OK? Well, you know, it's a very fundamental questions of the potential harms that your code might do.

You know, sort of basic ethics like this is basic, like, you know, the fact that something you're doing in the world can have unexpected consequences. And, you know, should you be responsible for this things versus something that's highly domain specific, like is scraping data off the web for your machine learning algorithm, you know, that is comprised of data that people don't know that there's being used for.

It is, you know, is that OK? So there are lots of very specific kinds of things that I think it be interesting to return to. And for example, you know, A.I. or machine learning ethics, I think should be its own course. And it should probably be, you know, towards the end towards where the students are actually doing advanced machine learning to be, you know, to reinforce those connexions.

Probably there's actually a plan underway and not now that Milo and Chris and my colleague at the college are on board. The plan is to catch a course on A.I. ethics, which will be taken by our students doing computer science and philosophy, probably compulsorily, but will also be available to others. And I think that will be very exciting to see those those developments. So, yeah, and I'm sure comfortable things will be happening elsewhere.

I want to focus on a different question, the two questions that have come in that are kind of in a similar sort of theme. So one from Allison, how do you integrate ethics into a graduate programme like Oxfords, where students are more likely to work on ethically questionable projects without the time to devote to an ethics class? I mean, I my quick responses, I think graduate have more time for an ethics class rather than less, because graduate course gives lots of time for reflection.

But the main the main issue here is that they could be working on ethically questionable projects. And a related question, your discussion is focussed on decisions that students might make as individuals when they create technology. But we know that it's often bigger forces, incentive structures, corporate cultures that are the real culprits when technologies have bad effects. What does your ethics pedagogy have to do with that? That's so if you focus, focus on the incentive here.

There are a lot of financial incentives to do unethical things to the student. Your teaching may have the best motives in the world, but when they go out into the job market, they come under a lot of pressure and maybe even, God forbid, when they are pressured by academics and they're doing their research. I hope that would be less of a problem. Milo, do you wanna have a go at this one? Yeah, sure. It's already two parts of it. One is how this fits within the education itself.

Sort of like more structure of you thinking about organisational incentives. You know, the move fast and break things. Culture of Silicon Valley that like that Max alluded to. One part of it, I think, is having these elements from social sciences and the areas of the humanities that look at kind of these things to teach students that how this is going on. Why does it grow? Why might Facebook do things that are unethical? Well, they have a fiduciary duty. It's part of their charter to make money.

So they get students to understand that, to get students to to see those kinds of things. Also not to be super deterministic about it, to see that things like corporate culture and how you set up a team if you're working with other people. These all things can have effects. But to get them to appreciate and not just to have an individual's view. So that's sort of like on the structural question.

And then there's this other question of how do you motivate the students to not just do the bad thing once they leave? That's a really hard question. And that's kind of this idea of the slogan I was using is like ready and willing. And I and I think, you know, it's gonna be context dependent. It's going to take work. And it's really hard to measure. But there are lots of different ways, like, you know, maybe them.

It sounds like, you know, some of the talking about how that I-Max had success with teaching parts of moral theory to show students that their intuitions have theoretical backing and then that makes them take things more seriously or just gets them to see that they have a certain power over the ethical outcomes in these small decisions that they're making. That might be exciting to them. We just have to explore, I think, there.

Thank you. Hello. Yeah. I think the so the first question about graduate students. If the if the system for the graduate student education is working properly, then there are various places where it can be picked up if they're doing something that could be ethically harmful. Obviously, they have the relationship with their supervisors. They have internal examinations as they go through the stages of their career where these things can be picked up as well.

They also have opportunities to take courses in their first year of graduate study, too. And a lot of them do take some of the ethics oriented courses when they take it. And we do, I think has been a very helpful development in our department over the last couple of years is that we have our own research ethics committee now so that if you're doing work that involves human participants,

it goes for an ethics cheque within the department. Whereas previously I was having to go to the Social Sciences Research Ethics Committee, and it's it makes a big difference because actually we need to have these. Are you okay? Hello, I'm back. I'm sorry, I don't know what happened. I just disappeared. Maybe I said something and somebody out.

Did it mean to me? But I still say it's very important that I think that people's work in computer science is checked by people have the expertise to know about the ethical dimensions of the technical work that they're doing. So I think there are various developments, very safeguards we have in place that if they all work properly, then there are protections against our graduate students who are more independent in their work, which I think is where the question is coming from.

We do have these sort of protections in place. And I think the question about sort of individuals and incentives is a really important one, and it's one that some of our students rights as well. I say, what was the point of being ethical?

If I go and work for this big company and what they're doing is deeply unethical and we tried to to raise it in the discussions, in the sessions where we talked about sort of codes of practise and the responsible innovation perspective, we use much more sort of society based where you might think about sort of, you know, safeguards or responsibility practises that could be embedded within organisations.

It could be in the form of self governance that industries might put in place their own codes of practise, or it could be policy dimensions as well. So we try to bring out that that wider perspective because it's absolutely true. You know, it's not just down to individual people to make ethical decisions. It's actually about how these things fit in across society, in the various institutions in society as well.

I'd like to bring in a couple more questions. So we've got Richard saying regarding the skills required for practising engineers. What about the socio political skills of asserting and applying ethical thinking when working with people who are less aware or motivated than you are? I mean, that's very pertinent here, isn't it? If if students from studying ethics can not only get good at thinking about it themselves, but also good at persuading others to take it seriously.

And if we could wrap that up with another couple of questions. From Anna, given how ubiquitous the problem of replicating structural inequalities seems to be a lie. Why is the sector so quiet about its role in these issues? How is the sector ensuring accountability and related to that? Do the speakers have any reflections on the decolonisation process of ethical algorithms? How can the industry's creators ensure marginalised communities are reflected in the creation of these algorithms?

So, as it does a few issues there, but there are obviously closely related. If each of you could just quickly say give your two penn'orth on that, Max is so really great questions. The first question dealing with the socio political issues are also exacerbated by the cultural dimension as well. We have had students who explicitly say, well, it's OK if women don't have rights, they don't have rights in my country. And, you know, and things like that.

And the very interesting thing about this is to be able to say, you know, well, you know, we're talking about this context and in this context, these these are the kinds of problems for which, you know, you know, in the West or whatever. And so I think it's very important to contextualise that. But also, I think that some of the the more fundamental moral philosophy gives us a lot of foundation or ammunition by which one can say,

you know, people should be treated equally and these things. And so I think that was a that sort of a a a useful thing to say. You know, if you believe these things, then then, you know, our systems are not in accord with what they're trying to achieve. And so that, I think, was very sort. What was this what was the second point? I'm sorry. I'm already did it with diversity and so forth in diversity. Oh, what I said. That is that is that is a hugely usually important problem.

And you know, what's interesting is that there are really bad ways to go about it and really good ways to go about it. And so right now, amongst. So there's a real issue with dealing with information economics. So the most sort of wealthy companies, the biggest you know, the power platforms have the resources to end. What they're trying to do right now is explicitly take task forces to go collect data on specific intersectional groups.

You know, that are, you know, in in all around the world, they have the money and resources to do this and they're going and targeting it to try to improve the overall performance of their eye systems. Smaller companies don't have that kind of resource, and they therefore rely on sort of public datasets which have this inherent, you know, large scale bias.

And the question is, do commercial companies like Google and the other large platforms have a moral imperative to share this sort of data that will allow systems to work more universally with the companies that they're competing with, these upstarts and stuff? So it's it's very much an economic problem as well. And you also mentioned the issue of companies obligations. If companies see their obligation as being to maximise shareholder value, that pushes them in one direction.

But if actually countries get wise to the fact that they need their companies to adopt a broader, broader aims with more stakeholders in mind. That could could help Helena or Milo. You want to say what, Helena? Yes. So this question about how do you persuade others? I've been thinking about it and it'll be a fantastic task, actually, for our students to do to kind of role play it out.

Imagine you've you started working in Company X and you're aware of all of these because can justices' how do you persuade your new colleagues of it? And I think it would be a wonderful practical challenge that we could set our students and maybe one that they would use when they go into the workforce.

And then the questions about structural inequality. You know, I think that is so crucial and there's a very large awareness of them within ethics at the moment about how these inequalities get reproduced through these systems and problems of lack of diversity within datasets and so on. And I think if that industry isn't doing enough, isn't being isn't doing enough. It's because it doesn't need to at the moment. So, you know, it can still make profits even while this is in place.

So in our economic environment, there has to be pushback for them to make the change. And I think we have seen some successes in icey team is, you know, social media platforms being held to account and making changes based on sort of public disapproval of appetising and so on. But I think, you know, ultimately we have to find ways to make diversity profitable and lack of diversity lose these companies money,

and that's what will change it. So it has to be sort of, you know, pressure coming, I think, from wider society, right? Yes. Miley. Yes, so on the first point of sort of communication and negotiation. I love that. And I think that that is sort of like one of the skills that you need to have if we're going to effect change doesn't. So let's say you're really good at identifying ethical issues. You're really motivated to do it, but you can't convince any of your co-workers to do it.

So what you mean you might have a little impact, but unless you're running the company and you might not be much. Yeah, I think, again, this is a matter of practise, the kind of thing that I Laina talked about as the kind of thing that we've done some. So we'll have a problem site where students will have like a decision that they've identified and they'll have identified certain stakeholder groups that are adversely affected.

And we'll have them like write a Facebook post address to that stakeholder group saying like, here's the decision and why. And then we'll ask them to imagine being the stakeholder reply to Facebook posts and do like a little dialogue back and forth. Exactly. That kind of thing that Helen was talking about. So I think one student's need practise and two students needs sort of tools or vocabulary. So in some cases, I agree. I can be the language of moral philosophy.

It has a weight behind it. It has a credibility. This isn't just my opinion. There's there's a there's a there there. And then I think the better you can sort of articulate what's going on, the better. The bigger repertoire you have of cases, the more you know about these things, the more articulate that you are about saying here's why this is the right choice. Because I've thought through the space of possible options, here are stakeholders.

Here's how doing this might affect them in this way. Or, you know, probably or here's how going this might affect them in that way. Sort of just having the skill to do that puts you in a better position to advocate for the choice. So I'll leave it there. And I'm running a little long and I just agree with everything that both Max and Holiness said about the diversity and inclusion elements. Thank you. Well, we're going to have to wrap it up, but I got one question, a a nice one to finish on.

I think if you can can each have a stab at coming up with one example. We've had examples of problematic algorithms, but do the speakers have any examples or illustrations of design that he's doing ethics? Well, so let's end on a positive note. He's going to volunteer to go first. I think the answer isn't no. Certainly. Yeah. What I can come up with an example, something at M.I.T. it's not a it's not an algorithm, but it it connects to this sort of ethical hackathon thing.

So it's a project called Make the Breast Pump Not Suck. So it used these methods that Lena talked about maths. Such On2 is sort of responsible design, participatory design, where people were getting together and being like, here's my experience with the breast pump. Here's what I would need out of it, both from like a financial perspective and from a user perspective. And that resulted in a new prototype of the breast pump that's since been developed.

So, I mean, it's like there's not something inherently wrong with technology. There's a lot we can do. And there are a lot of known methods for pushing us towards outcomes that promote social good and don't just sort of act as some sort of guardrail.

Thank you. There have been a variety of really exciting experiments and experimental efforts in trying to reduce polarisation on social media that have that are really exciting, you know, things that that allow people to discuss in a sort of long form, why they believe what they believe rather than to in order to counter the sort of reaction driven incentive engineering that we've been exposed to all of these years.

And I think that, you know, I would point to this one particular project, but there is a large number of projects that try to apply information visualisation, as well as other sort of techniques to ask people to actually spend time flow down and then spend time arguing their point and and so that they can try to to try to then counter and discuss things in a more civil way. And I think that's really, you know, what we could do with that elsewhere in America. Yeah. Eleanor? Yeah, I do agree.

There are various examples. And I think actually the point that Milo was making about the value of of products at about three participatory design process is so you where you bring in users and you try to understand their perspectives and you know, their experiences. Right from the early stages of design can make surimi beneficial effect for producing products or systems that promote social good.

The example I'm gonna give is someone I was talking to just last week who works in robotics and was producing and developing assistive care robots that could interact with people with with older people in care homes and provide sort of social support and other forms of support. But in this pandemic scenario has diverted attention towards a very simple cleaning robot that can go into hospitals and provide cleaning cleaning services to to assist with infection control in hospitals,

checking the pandemics. I think that's a very good example. And the robot functions without collecting up any personal data or anything like that. So that's my example. Excellent. Oh, that's a nice note to finish on. Thank you. Thank you very much indeed to all of you. That's been a fascinating discussion and it's raised a lot of important issues.

The sessions being recorded, it's going to be added to what's becoming a very rich resource, covering lots of different areas of ethics that we're building up at Oxford. If you want to look at those resources, by the way, if you go to the Faculty of Philosophy website and the ethics section, that's philosophy dot ops, dot, ac dot, UK slash A.I. Ethics, you'll find it there. There are also links to it from fellow komp dot net that p h i l o c o m p dot net.

And if you go to the ethics section there, you'll find links. We're trying to build these up so that over time the seminars that we've given will become a really valuable resource for people both in academia and outside. And I hope that what we've produced today will feed into a much broader discussion. And all note will no doubt develop hugely over coming years as we all learn more about the possibilities and problems of applying ethics in A.I. developments.

And in particular, as we've seen today, teaching and promoting such applications. So thank you again to the speakers. Thank you, Milo. Thank you, Helena. Thank you, Max. That's been a tremendous discussion. Thank you for tuning in and especially those who ask questions. And it's been great in particular to see how some of those questions have even here now in this session, inspired some fruitful ideas for further development of courses in ethics.

Look out for our next seminar, that's on Thursday, the twenty sixth of November. Same time, same day of the week, five o'clock, and that will be on a I and autonomy. So until then. Thank you very much. I hope you've enjoyed this as much as I have.