I am here to represent all of social science that Allen didn't cover and and to all of the work that we do at Oh I in 15 minutes, I had a good representation at the last one. Oh, good. Two sessions, I'll I'll cut those lights out. I am a sociologist by training, and I'm here really to make a call to arms for why we should be studying AI in a particular way.
And that's not to negate the my other speakers tonight who are doing fascinating and wonderful topics for all of you who are coming together across this research community in various ways. But to make a call for something I see that's not yet part of our discussions about ethics. And that's really to be to talk about users use and the social context.
Now, as a sociologist primarily of work and organisations, my view on technology has been deeply shaped by how things get used in practise, and I'll talk a little bit more about that tonight. But we have a discourse. It will be no surprise to anyone in the room that we are talking. When we talk about ethics and AI and future A.I., we we talk about fears that get translated very well into public discourse about what A.I. is, isn't as we'll hear from my colleague Rasmus Nielsen later.
We also have a problem with how the press and the media are talking about A.I. and who is talking in those discourse. I would argue that we within the academy have a an urgent need to help address and prepare and think about that short term because, you know, as John Maynard Keynes said, in the long term, we're all dead right now. The near-term impact of how artificial intelligence is going to impact society is something that we're not necessarily bringing a toolkit for people to understand.
So we're talking to one one one way about technologies that aren't here yet. That's great. We need to be having those conversations, but we're not. We're not helping to prepare the public. We're not helping to engage around questions. And what do we need to do? I think we need to get much more tangible. Part of the challenge for us as researchers, is when we talk about artificial intelligence and machine learning.
We're really talking about becoming social infrastructure. And it's that social infrastructure, that's social material infrastructure that's helping to shape a whole host of what Karl called second order effects, what will be the effect on jobs, what will be the effect on politics? How will our society be structured? It's those questions that are driving the concern about AI and and yet as scientists, it's hard for us to get into the nitty gritty of studying that infrastructure.
Now the researchers Stuart Russell has a has a beautiful metaphor about infrastructure and A.I., and some of you may have heard this. So forgive me if you if you have, he says. You know, it's as if we have artificial intelligence. You know, think of think of people who make artificial intelligence as asphalt engineers and they're really, really good at making asphalt. And so they just go around saying, you know, your garden, it needs asphalt that beach over there, asphalt.
Why that road? It's in the wrong place. Let's put asphalt here instead. In some ways, our policy and technical conversations about expertise in machine learning and AI are being dominated by people who are very good at the engineering,
but not necessarily as good. So we as kind of a broader community, I think, need to work on multi-disciplinary ways that we can address and redress some of the challenges of how we're going to have those hard and difficult questions around the normative political and distribute of questions of how we're going to talk about resources.
But that leaves our challenge. If we see these systems, machine learning and artificial intelligence systems becoming part and parcel of how we run our daily lives without things being visible with things being black boxed to most people, then how do we, as social researchers, start to study and understand that? And I think there's a twofold problem that I'll give to you now. This is from exactly a year ago this month I ran a workshop, an expert workshop.
It was a room not unlike this in Silicon Valley, with the researcher from the University of Washington, Javan West. Now Jevon runs a course for those of you who are not native English speakers. His course is called calling B.S. and he actually says, I want tonight what B.S. stands for, but it means to call out to call is wrong tomfoolery or outright lies or trickery, something that someone has done.
And so he has been featured in The New Yorker for this undergraduate course in teaching people how to read statistics right? Know how to live with statistics, but how to call B.S. with statistics. And so we together ran. It was a group of 40 experts in the room. Some people from academia, some people from industry, some people from advocacy. And we basically said, OK, the rules of the game are this you have to call in the first half hour, you have to call B.S. on A.I.
Now this is akin to what Arvin Iranian has has called, you know, the snake oil of A.I. Now we all know it. Those of us who are experts, we've seen the cases in which something is called A.I. and it's an Excel spreadsheet or something is called A.I. And you know, the challenges really are that there was some political corruption going on. And so and so that's what this was about. It was about calling out these cases. And let me call your attention.
I don't expect you can read what these words. So in 30 minutes, we got twenty seven different cases. Some of these cases were common. Lee known challenges of how biased data lead to biased outputs and that there's some kind of gap between what we think the model is doing and what the data can actually show for. So, for example, Jupiter Medical, IBM, Watson for oncology and a challenge where doctors thought they were looking at a real time, a diagnostic tool.
The diagnostic tool was actually done on synthetic cases or Amazon famous case of creating a hiring algorithm that ends up discriminating against people who have anything in their CV that recognises them as women. So this is this is a challenge that we actually now have language for right now. In Barcelona, the Fairness, Accountability and Transparency Conference is is launching this kicking off as we speak.
And this idea that biased data lead to biased outputs is something that we, as researchers now can. We have language where we can talk about this problem and the problem has awareness because we've been raising this. The second kind of classification of these problems that were identified by experts was really growing pains.
This is we are in a phenomenal period of growth as a certain Nigel pointed out of how computing technologies and large corporations are at a particular moment leading to an expansion of use cases. And of course, the industrial applications are a little shaky, and we're in a relatively novel phase of where this industry is going.
Some of that will be worked out over time growing pains, but it's really these last three that I want to call our attention to because of that of the other types of of challenges to classify these challenges. The third was really a kind of mismatch mismatched expectations that experts or designers have one set of expectations or are more sanguine about the possibility of particular modelling exercise.
But you know, the public, they just don't understand or the sales team, they just don't understand. Or, you know, the vendor knows that the client doesn't know that there are these difference in product and expectations. The fourth was really, you know, if we could only fix the perceptions of people about what I really is, then somehow we could get into a, you know, we would solve and resolve some of these problems.
And the fifth was a kind of bad application right there. Bad users out there in the world. So good users know how to use predictive policing and software and ways. Is that it's ethical, but you know, there are bad police departments out there that will use these things in a bad way.
So I would say that these three really are about a kind of granularity around use users and social context and from the perspective of the social studies of technology from the social science of technology, we actually take that as the wheelhouse of what we do so that we never truly think of technologies as separate or separable from how they get used and what their social context is.
So we've had and I'm going to just speed through this because this is kind of where I think some of the near term, that wonderful near-term long term divide that we have. I think this is kind of where we are. We we know that we have ethical challenges with enormous perspective that have given us a way to talk about the design and the rolling out of AI technologies.
But what I would argue is that this kind of notion that we need to bring back the users, the uses and the social context for those is really part of what we're missing and some of the conversations about about ethics. So let me let me bring in three of those perspectives from that world of research. The first, and this is a long standing in the sociology of technology. This is a long standing idea that technologies are never finished by the designer.
They're always finished in their use. They're open to interpretation, to modification and adaptation. And sometimes those those changes stick, right? Sometimes they're durable and they actually influence the long term trajectory of the technology. And sometimes they're just hacks. And the trick is then figuring out when, what, which, which one is going on. The second and again, this is a 40 year finding, right? This isn't this isn't new.
It's certainly not mine, but that new technologies always become an occasion at work for reasserting and asserting power and expertise. Right. So so we never just plot new technologies in and the boss says, Go, go do it. We always have these kind of opportunities to negotiate and renegotiate. And what we've seen is that those can really diverge pretty radically, even with the same set of tools, even in the same kinds of organisations.
And the third is is a conversation that I am really a part of and kind of reimagining on what we call technological affordances, right? So when people approach a technology and I and I use a audience here, both in a way akin to Don Norman's notions of importance, but also from a sociological perspective, when we when a user approaches a technology, you know, they're they they they see things that they can do with it, right?
That's what we think of as a technological. They are afforded certain opportunities. But the research that's coming out now about affordances really suggests how people people's ability to take up technological affordances are deeply shaped by their social position. And the work that my team is doing is really deeply shaped by where they are and their social, organisational and institutional context. So, OK, the technology doesn't just matter.
It's where the user has that deeply, deeply matters. So let me give you a concrete example. Literally, this is concrete. I've spent a decade working in digitisation and construction, and this is a picture from my field, one of my field sites. This picture is an incredible accomplishment in and of itself that unionised workers would accept what they saw as potentially disruptive technology into the field site.
Now, when we started this project, we had a we created a computer folder, as you all do that, said BIM hype. This tool is called building and for modern building information modelling or ben. And so we had our hype folder and we put everything in there and literally everything that everyone was being told in the trade press about the tool you're looking at was this would completely and utterly revolutionise how architects, engineers and builders work together. Fast forward a decade.
I am privileged to have spent a decade on this project. You know, we kind of show in part something that we know from sociology of technology, that the mental models and the structural elements, those rewards norms and cultures of organisations really fundamentally shape how the technologies work. And I don't mean theoretically how they work. I mean, how they actually work in practise is as much. Apart of those mental models, as it is how people are, are told or forced.
So the uptake of this of this tool was nowhere near as transformative as the industry hope said that it would be. Let me give you another from a different point of view that doesn't take a decade. A paper that I have been talking about quite a bit written by colleagues at Stanford and Cornell.
Take off from Kai from last year. So what I love about this paper is it's a granular paper of lifting the algorithmic lid, lifting the black box lid off of a study to help get people's perceptions of what is going on with AI. Now they discover what they call the replicant effect. That is, when you show people Airbnb host profiles, you can ask them an experimental setting.
Who do you trust? And then, when shown profiles again, say some of these are written by AI and some of these are written by people. They find trust collapse. And that should worry many of us because we're used to understanding how people interact with, in particular, settings. But we're not quite yet there and understanding how the introduction of artificial technologies technologies transparently identified as,
quote unquote, A.I. Impacts the social organisation that people have around them, so that's freakish at all 2019 from. So in my one minute left, here's kind of my manifesto for where I think we need to go. We need many more case studies. The examples we have of AI failures. The examples we have of ethical problems are getting a little stale. We need to map, track and compare and measure these changes of people's practises with AI across multiple settings across multiple countries.
We need to identify elements of social social infrastructure and social structure that really serve as levers for responsible use. So, so, so in addition to the kind of ethical projects that we have going on, we need to think about what are those organisational routines that might help us get to better and we need to measure how people are responding to AI systems, including existing social norms, conventions, heuristics and social organisation.
And then I think we really need to be doing more comparative work. Our work right now overwhelmingly is dominated in the AI leading countries, and we have very little work that goes across or with emerging countries. So let me give you just kind of my set of questions. I really developed this question with Jack Chu and Madam Madeline Claire Ellis in a paper.
We did this this autumn. What and whose goals are being achieved or promised through what structured performance using what division of labour under whose control at whose expense. So you'll notice this is very much a question asking about what are the social impacts of any A.I. or machine learning system. And finally, my kind of guiding principles. This is this is for me, and I'm just speaking for me here.
I really think it's critical that we need to be expanding our knowledge and ethical capacity outside of the communities of experts in science technology. It's critical for society at this particular moment. We need to participate with the communities of engineering, data science and AI communities, both in research spheres but also in commercial spheres. Again, that's that's a little bit of a dividing line, but I think it's critical for getting problem solved.
And then finally, and perhaps most urgently from my perspective, is, you know, I really think we have to be looking at AI on the ground and we have to be looking at these questions of how he is being used in that social context in order to actually understand what is developing and what's going on. Thank you.