If people are designing these systems to cause harm fundamentally, then there kind of is no way to make a human-centered version of that sort of system.
SPEAKER_00Made in partnership with The Nation magazine. I'm your host, Paris Marks, and on this special holiday-ish episode, because uh I'm not recording anything new over the holidays, I figured I would release one of the premium episodes that I have been making available to Patreon supporters from our data vampire series in October. And I wanted to start with this interview with Ali Al-Khatib, who works for Logics magazine and was previously the director of the Center for Applied Data Ethics. Now, if you listen to the series, you will have heard clips from this interview with Ali, but I thought it was a really good one on AI and, you know, the kind of political aspects of AI and the broader consequences of this AI hype and the rollout of these AI systems that really gave me a lot of food for thought and that I heard really positive responses from the Patreon supporters when they heard it as well. So I figured if there were going to be some premium episodes that were going to be released to everybody, I wanted to include this one so that it could enter, you know, this broader conversation and maybe get you thinking about some other aspects of the AI conversation by going beyond what I was able to include in the Data Vampire series. So I hope you enjoy this conversation with Ali. And since this is the final episode that will be published in 2024, I also wanted to say a special thank you to Bridget Pelou Fry. You will probably have heard her name in the outro to the show if you, you know, frequently go that far. She has been helping with the transcripts for the show for the past like couple years now. And she has been doing a fantastic job. We now have more than a hundred transcripts available on the website for previous episodes. There has been a little bit of a delay lately because we have both been very busy. And I wanted to say a special thank you to Bridget because she will be finishing up with the show at the end of 2024. And so, you know, just a big thanks for all the work that she has been putting into this to make the shows more accessible to people by having these transcripts on the website. I've really appreciated the work that Bridget has done for the show over that time, and I wish her all the best on her future. And so, with that said, if you enjoyed this episode, make sure to leave a five-star review on your podcast platform of choice. You can share it on social media or with any friends or colleagues who you think would learn from it. And as always, if you want to support the work that goes into making Tech Won't Save Us, and to get access to future premium episodes, because we still have a bunch that we need to release from the Data Vampire series in 2025, you can join a number of supporters, and I'm gonna say quite a few names here because I realize that I let the list really kind of back up lately. So you can join supporters like Lucas from the Czech Republic, Don from the Netherlands, Areta from New Jersey, Ed from Limerick in Ireland, MV Romana from Vancouver, who uh was also recently on the show, Alice from Boston, Kirsika in Finland, Scott from Philly, Cam in North Pole, Alaska, Deb from Guelph, Ford from California, Peter in San Francisco, Jessica from Seattle, Washington, Emery in Portland, Alan in Toronto, David from Spain, Ruth in New Zealand, Julian from Austria, and Katie in Kelowna. By going to patreon.com/slash techwon'save us, where you can become a supporter as well. Thanks so much and enjoy this conversation with Ali Alkatib. What do you see as being the biggest problems with generative AI and the hype around it right now?
SPEAKER_01So I think that there are a couple of problems. One of them is that the nature of the data that you need for these kinds of systems is uh both in scope and in kind so far reaching that it sort of makes impossible a serious conversation about consent, um, about the consent of the accumulation of all of this data that's that's out on the web in public, the acquisition or the elicitation of consent from people who are using uh systems and services that they need to use for myriad other purposes in society and in life that they can't meaningfully consent or withdraw consent from, and sort of the broader sort of like understanding of how are these, how is the data that's collected around me going to be used, and do I consent to the speculative uses of those data? So that's like one element of it. Um I think that another major element of the problems with generative AI is that the entire design and application of these large language model systems, these generative AI systems, is kind of inherently speculative and sort of undefined or ill-defined. And so it needs to be highly generalized. So people will say that this system is as intelligent as a high school student, but that's not a benchmark or a definition that means anything to anyone in the serious kind of academic evaluation community. So it's sort of just a freehand that kind of means nothing. And that makes it very difficult to either like design a system that is effective for any specific task, or even to evaluate this generative AI system for any specific task. And uh it makes for this very problematic shifting sand of trying to build anything on top of it that's just kind of impossible.
SPEAKER_00Fascinating. What do you see as the biggest harms that come of these generative AI systems when they're deployed out into the world?
SPEAKER_01I think one of the biggest harms is that it offloads a lot of the decision making and particularly a lot of the human discretion that needs to be exercised to algorithmic systems, uh, which can't exercise discretion, at least not in any way that we understand it or that we think about it, and certainly doesn't understand any of the peculiar qualities or characteristics or anything like that that we have and live in our actual experience. So these algorithmic systems are just not capable of comprehending, understanding whatever word you want to use, the peculiarities of what we're dealing with or what what our life is and the things that make our circumstances unique from other circumstances or other cases that it might have in the training data. Um, that makes it very difficult to get any kind of semantic, meaningful justice or the correct kind of decision, the spirit of the rules or the patterns that these systems are trained on to actually manifest in real life. And then it also makes it really difficult for people to, even people who are using these generative systems to kind of quote unquote inform their decisions. It makes it difficult for them to rebuke the recommendation that the system makes, which means that people are technically in the loop, but they're actually not really in a position to exercise that authority in any meaningful way, which makes it uh, again, very difficult for people to imagine a more just world, imagine a more a better future of the world. And so instead they sort of have to follow somewhat blithely the recommendations or the emissions of these of these algorithmic systems.
SPEAKER_00Yeah, that's fascinating. And it makes a lot of sense as well. It's also it also makes you kind of concerned about the broader impacts of that if it does become, you know, uh adopted at scale, as so many of these companies expect and and kind of want us to do, right?
SPEAKER_01Yeah, certainly. Uh I mean, one of the things that is both a challenge and uh possible kind of positive future or a positive kind of reality of having a lot of street level bureaucrats and having people who are making decisions in the world is that they can situate their experiences and their knowledge and they can learn things as they're going. And monolithic algorithmic systems simply can't really do that, especially with these large language models that you can fine-tune at the edges in incremental ways, but really you can't do sort of deep kind of retraining of these, again, of the foundation models or of the basic kind of foundations of these systems, which makes it very difficult to seriously talk about reforming these systems in any meaningful way, or asking uh these companies that have hundreds of millions or billions of dollars invested in these in the training of these systems to radically rethink the way that they go about doing anything. It's already difficult to do with bureaucratic systems and institutions, which is one way that I tend to think about algorithmic systems. The human sort of social structure is quite difficult to change and fix and reform, but it's at least conceptually possible in a way that's much more difficult when everything is even more localized and more sort of compressed into one locus of power.
SPEAKER_00You know, there's been a lot of talk in the past year and a half about what effective regulation of AI is going to look like or should look like. Um, you know, a lot of debate about those things, uh, a lot of CEOs speaking out and uh saying what that should look like, and unfortunately lawmakers uh listening to them as though they have the answers. Um, but you recently wrote an essay about destroying AI, you know, taking one step further than that, than just kind of passing some regulations to try to, you know, uh reduce the worst aspects of what these AI systems can do. What brought you to the point to write something like that and to take that further step?
SPEAKER_01Yeah, sorry. So I've been studying human-computer interaction for about 10 years, started a PhD program 10 years ago today, actually, uh, or close to today. I mean, I had been spending a long time thinking about how to develop human-centered systems and particularly uh writing papers that were trying to bring ideas from the social sciences about power, about oppression, about violence, um, into understanding how algorithmic systems can manifest these kinds of harms and trying to encourage people to think along those kinds of lines to understand and then to design uh consequential algorithmic systems in various different ways. Um and part of my frustration was coming from the feeling that HCI was sort of not picking up some of that. Although I suppose I should be grateful that I like a lot of my work has been very well received, um uh like especially sort of like in the quantitative and like sort of awards sense and everything like that. But it really just feels like uh a lot of the work that I had done sort of didn't sufficiently or didn't adequately address what was sort of a core problem with these systems in the first place, which is that if if people are designing these systems to cause harm fundamentally, then there kind of is no way to make a human-centered version of that sort of system. In the same way, legislation that makes it slightly more costly to do something harmful doesn't necessarily fix or even really discourage uh tech companies that find ways to uh amordize those costs or kind of absorb those costs into their business model. One example that I think I've given recently in like conversation was that there are all sorts of reasons or all sorts of like kind of powers that cause us to behave differently when we're driving on the streets, because as individual people, the costs of crashing into another car or of hitting a pedestrian or something like that are quite substantial for us as individuals. But if a tech company that's developing autonomous cars is going to put 100,000 or a million cars out onto the streets, it really behooves them to find a way to legislatively make it not their fault to hit a pedestrian, for instance. And so they find ways to sort of defer the responsibility for who ultimately caused that harm or who takes the responsibility for whatever kind of incident or whatever. And so that creates like these really wild perverse incentives to find ways to sort of consolidate and then offload responsibilities and consequences for violence. And I just don't see a good way with design out of that, or even with a lot of legislative solutions and everything else like that. And so I kind of wanted to explore uh what I thought was a somewhat uh like a not a not overly provocative suggestion, which was that uh sometimes a person will encounter an algorithmic system and it is not going to stop hurting them and they will not be able to escape the system. And given those two facts, I think it's pretty kind of obvious that it is reasonable to start dismantling the system, to destroy it. And I'm not saying like we should necessarily destroy everything that has silicone in it or something like that, although I'm sure there are probably people that would argue that and I'd be happy to hear them out. But it doesn't seem radical to me to say if you can't leave a system, if the system is harming you, if you can't get it to stop hurting you, there really aren't that many other options. And like I think it's reasonable to say you don't have to take it. You don't have to continue to be to be harmed. And if it forecloses on all of the other possible avenues that you have, then one of the avenues that we sometimes don't like to talk about is to start destroying the system. And I think that as a person trained in design, sometimes I see papers where people say, maybe the consequence, the implication of this research that we're doing is that we shouldn't design systems, or maybe what we should do is dismantle the algorithmic system. But these are the conversations that designers have amongst themselves and not necessarily a conversation that we're telling people out into the world out in the world to consider as a potential answer. And it occurred to me that I think I'm sort of struggling with designers of tech systems who believe that they themselves can be the arbiters of whether to dismantle an algorithmic system that's causing harm, but not the people that are affected by the algorithmic system or not the other, the people who are downstream of the system. And I think I wanted to sort of continue to explore that thought and say, if somebody can make a decision that the system should not exist, why can't the person who is facing it on a regular basis? And what would that look like? Or what would be the vocabulary that we would use or that we should develop to have an understanding of or an appreciation of the need to do that, the mechanisms that people go about doing that with? And how do we how do we make sense of that? Or how do we just understand that in general? Um, is it even necessary to understand it? Or do we just have to accept that that is a thing that people will do sometimes and that that's not, I don't even want to say that's not malicious, but it's certainly not something that we should be trying to rebuke or challenge or fight.
SPEAKER_00It's interesting to hear you explain that, you know, explain what led to you to write that and then to think about how wild it is that it's so rare to talk about dismantling or challenging AI systems in that way, like, you know, there's plenty to talk about regulation and plenty of talk about, okay, how something is bad and we need to do something about it. But the idea of actually going and targeting that system and trying to dismantle it is something that's so outside the realm of like the usual discourse when it comes to technology. And it strikes me as odd that that's the case, that it's something that doesn't come up more in these discussions.
SPEAKER_01Yeah, I think we're I mean, I understand it. And like I I'm not like I'm not from some other planet. Like I totally get that it's like uncomfortable to talk about like we should just destroy this thing. Like that seems violent. It seems scary. It seems what do we do with the wreckage afterwards? I don't know. Like um, this is you know an entire space that's like uncomfortable to dwell in. But I mean, I don't know whether this analogy is very good. But like, so I live in Ann Arbor and uh it's a small town that sometimes inflates to like five times its size because there's like a hundred thousand people that come here for football games and things like that. And um I sort of I was kind of workshopping this thought that like, what if there was a restaurant in town that was constantly getting people really sick? And the uh health inspector, for whatever reason, was just not doing anything, and uh no law enforcement was doing anything, and visitors would just keep coming and getting really sick, and some of them getting really seriously, like their lives would be derailed in like increasingly catastrophic ways. And there was nothing that we seemed to be able to do to affect change about that restaurant sort of like operated or anything. And the reality of how people are engaging with this city was that like they've sort of like passed through and just didn't realize that this is a you know a restaurant where people go and get sick or something like that. I don't think it's crazy to say like we need to take matters into our own hands. Um I don't think that like the idea that the the market will solve this problem will sort of support that in this case, because that restaurant really only needs to like make sales once every couple of weeks and they're gonna like they'll be able to coast through the next month and they will continue to do harm. They will not improve like get better or anything else like that. I think like the the way that I like was thinking about this with regard to algorithmic systems was that these systems are not going to get better if we live in, let's say, a regulatory environment where the FCC or various federal regulatory agencies or even international regulatory agencies like the EU are just unable to wrap their arms around what these tech companies are doing, what these systems are doing. And in particular, given that people will suffer in the meantime while these organizations are trying to do something about it, it just seemed sort of reasonable to say, well, of course, there are things that we can do in the immediate term to try to mitigate that harm or try to like sort of stop that harm. I think, yeah, it certainly is uncomfortable to think through and all of that. But at the very least, it should be a credible possible outcome that um somebody will stop you if you try to hurt people. Uh and it's not just that you will face consequences later, but that somebody might just stop you. And of course that's uncomfortable. I don't like confronting people. I don't like stopping people from doing things, but I also don't like them hurting people. So like it's kind of a catch-22 that we're in.
SPEAKER_00Yeah. No, I I think it makes perfect sense. And I think your your analogy works for me. What would it actually look like then to sabotage an AI system, to destroy an AI system, to make it stop doing the harm that you're talking about?
SPEAKER_01Yeah, there are a lot of a lot of ways, right? Um, so I think like one of the things that I have thought about has been um sort of a framework that I guess is like better known from like how to blow up a pipeline, which is not just about how to blow up a pipeline, but about how to change the political economy of operating oil pipelines, uh fundamentally changing the costs of operating these systems, of operating extractive greenhouse gas emitting like businesses. And I think that what we need to do is shift how costly it is to run these systems completely irresponsibly. And so I think one of the things that people can do in the sort of immediate term is find ways to subvert or get around algorithmic systems when the system is harming them. Find ways not to provide those systems with the data that the creators or the designers of these systems are trying to get from you. Um, find ways to make the system more costly or less effective or less kind of advantageous in whatever way you can find or or whatever, um given whatever circumstances you have. And sometimes that means sort of intentional work slows, like slowing down work or work stoppages. Um sometimes that means feeding false information to the system. One project that I was really excited about, I think it was at the University of Chicago, Glaze, which is basically a system which, at least as at the time that we're talking about it, sort of inserts various sort of like non-visual artifacts into images to make machine learning systems that are that encounter these images and to try to train on them without the consent of the artist, basically sabotages the model in various ways and causes all sorts of weird artifacts to emerge and things like that. And that's not just a way to encourage the designers of these machine learning systems to actually go back and get permission. It's a way to make it more costly for them to run these crawlers. It's a way to make it more costly for them to incorporate these images into data sets. It's a way to make it more costly for them to run these generative AI systems, completely sort of indifferent to how the data got acquired or collected, how the data got incorporated into these data sets and everything else like that. It's a way to make it fundamentally more difficult at every step of the way to do what they're doing, the way that they're doing it. Part of that might also include things like uh basically making a lot of the use of tech less effective, less uh efficient, things like that. But I try to be like mindful of the fact that people encounter algorithmic systems in like a million different ways. And I don't want to like give a prescription that says feed false data into every system, because there are going to be circumstances where if a certain individual is caught feeding kind of like bullshit data into a system, they're gonna face more retribution than I would in other circumstances, for instance. Um, but to think about ways to make it more costly, basically, to run these systems and to be indifferent about the consequences of the systems or the inputs of the systems.
SPEAKER_00In another essay of yours that I read, you talked a lot about accountability, um, in particular for the people who are creating these systems and how you know a lot of that accountability is not something that they feel right now, right? The people designing these things. Why is it so important for those who are making decisions that can cause these algorithmic harms to be held accountable for that when it happens?
SPEAKER_01I think that there are a couple of reasons. I'd like the most sort of like micro level is that when people make decisions about other people's lives, I think that they should internalize and understand the consequences on the people that they're making decisions about. And if they don't care or if they're allowed to be indifferent to what's happening to the people that they're making decisions about, that creates an environment where a person can be really indifferent and really callous about what they're doing to other people, to other human beings. And I think that's a bad place to be as a society. I think that hot take, we broadly should not support the ongoing administration of systems where people can make consequential decisions about the lives of other people with total indifference about what that does to them. But that's on the micro level. I think that that's like one small component of it. I think that if you think about, again, if you think about tech companies that run self-driving cars and they think to themselves, well, our 100,000 car fleet hits five, 10, 50 people per year. That's five, 10, 50 people whose lives are totally changed by the thing that has happened to them because of this tech company. But to the tech company, this is a cost. This is a question on a balance sheet about whether they should invest in lobbying that says that self Driving cars are not responsible for the consequences of hitting a person. And that's a fundamentally different kind of conversation than the ones that we should be having, which is what are the human costs of these kinds of systems out in the world? And I think that at a larger level, big tech companies do not have the capacity. They just are not built in a way in a capitalist society to fundamentally like to basically work on the human costs. They can only really work on these financial costs. And I think that like regulatory agencies or regulations in general that say, here are the financial costs to try to translate human costs into something that capitalist entities understand sort of fundamentally don't work. If you have a company that's big enough that they can spend as much money as they do in places like Uber or Lyft or whatever to actually rewrite the rules of gig work or actually rewrite rewrite the rules of accountability for self-driving vehicles, or actually rewrite the rules for an algorithmic system that determines whether to separate kids from their parents. A tech company that has millions or billions of dollars in this kind of industry of providing these services is going to find ways to make it cost effective to cause this kind of harm. And I don't necessarily believe that as a society, we're like adequately equipped to deal with that from the top down without any bottom-up kind of force to also make that cost more salient.
SPEAKER_00You know, we've been talking a lot about the problems with these systems, the harms that can come from them, you know, and why it's important to push back on that, whether it's through means of accountability or or directly sabotage them so they don't work properly. But if we're thinking about what a better future of these technologies looks like, maybe not talking about AI specifically, but a better future for the use of technology in a way that benefits the broader public rather than just being used in the way that these companies are using them, as you've been describing. What do you think that that future looks like?
SPEAKER_01So I think that there are a couple of like I guess entry points to this, right? So one of them is I think that we have like completely missed the exit like conceptually on talking about what a future would look like where consent is at the core of everything that we do. And it's not too late to like go back and revisit that, right? I I don't think that it is. I think that people who say that it is, that we are way past that or that the world is already baked in in that way or whatever, are just they're too committed themselves to that paradigm of skipping past consent and saying the information's out there or the data's already on the web or whatever. And so therefore, there's just no, there's no talking about whether we use somebody's uh data on a website because their spiders.txt file wasn't updated in time for our secret project that was crawling the web before we told anybody about it. But that's one element of this that we could say the core thing we're going to try to work towards is that consent is like part of the conversation. It is a central part of the conversation at every step. And that includes the collecting of data. I don't think it's necessarily impossible to have a conversation about collecting vast amounts of data from people who consent to that data being collected if the project makes sense to them, if the project that the people are working on is something that they want to participate in. The only reason that it seems unbelievable or like not credible right now is because we talk about systems that have kind of no speculative value to them. And so, of course, it goes without saying that nobody would consent to their data being collected for this kind of system because it's not clear what ChatGPT does. It's not clear what these systems do. And in that kind of paradigm, like, of course, consent can't be acquired because this is like a doctor saying, like, I want to do doctor stuff. Can I have some of your cells? And it's like, no, like, can you tell me what the research is? Can you tell me anything about it? Like, uh, and that was how we lived for a while. I mean, Henrietta Lacks is like an entire module for a lot of data ethicists, because doctors just took her cells without talking to her about it, without telling her about it, without getting her consent about any of it. And they make all these rationalizations about the importance of it and how it like produced all of these changes and helped uh medical science advance in all this. But like she was capable of cons giving consent if that was the case, if that was truly the argument that they wanted to make. Did they think that she was not intelligent enough? Did they think that she was not capable of making a compassionate pro-social decision? Did they think that only they were capable of adjudicating that? That that's crazy, right? Anyway, all that is to say that like in medical science today, for the most part, people look back on what they did to Henrietta Lax as a gross and vile transgression of human autonomy. And I hope that we can think about what kind of a future would look like to say, we want to do research, we need to use your cells, we think that it can be useful for this. Do we have your permission to collect a sample? That's not ridiculous. It's not impossible to think about that in the world of tech. Um, and so I think that's one part of it. I think that another part of it is, again, using technical systems to help us make sense of complex problems is not something that I'm like categorically against. I think that computational systems can be great ways of trying to sort of like draw comparisons between two fundamentally different things. But I think that when that system starts to become overly decisive or have an outsized kind of like weight of uh what influence it has in making decisions about consequential things, then it becomes obviously much more harmful and much more problematic and dangerous. And again, I think it comes back to this question of like, do people consent to the influence that the system has over this particular decision about my life? And I think that in a lot of ways, tech companies find ways to claim that people are not stakeholders in decisions that are about them, or they find ways to say, well, this person's just not informed enough or too stupid or whatever to make an informed decision that is for the benefit of society or whatever. And these are all terrible rationalizations that don't really even hold up to scrutiny today. And I certainly hope don't hold up to scrutiny in the future. And that I hope when somebody says that sort of stuff in the future, people can just immediately say, like, that's just ridiculous. Like, we just don't need to live like this. And that they can dismiss it without ever even taking it seriously in the first place.
SPEAKER_00Is there anything else that you wanted to add or you feel good?
SPEAKER_01I think like one thing that I kind of want to get off my chest, and like I'm happy to like have it cut or whatever, but I'm curious if you have thoughts about this, even. So I have been thinking about uh the critique that people have made about the destroying AI post where they were saying, like, you don't know what AI is. And I want to kind of come back with the blog post. I mean, part of the challenge is it's very difficult to top that blog post because somebody actually forwarded it to the cops at one point because they were that freaked out about it.
SPEAKER_00And I was like in a whole different city or something, wasn't it?
SPEAKER_01Yeah. Um yeah. So they forward it to the campus police of the university where they graduated. And I was just like, what is even going through your head? Like, I almost wanted to help this person, but I also realized like, no, I don't. Like that's that's actually self-sabotage. But yeah, so like how do you how do you write a blog post? It's better than that, right? Uh but um the thing that I've been thinking about has been how would I define what AI is? Because I think particularly as I have written about so in late 2020, maybe early 2021, it came out that Stanford had quote unquote like an algorithm or in some system or whatever to decide when and how the COVID vaccine would be like deployed to healthcare workers. And it was like prioritizing like senior researchers who were never on campus, who could work from home. It was like downranking people who were actually like in hospitals every day. And so the system was like obviously like roundly scorned. And then it sort of emerged that they were just using like a flow chart. Like it was like a thing on like an easel, like that they could just show that was like not, it wasn't an algorithmic, I mean, it was an algorithmic system in the most STS kind of way possible. Like science and technology studies kind of definitions of what an algorithm is, but it wasn't machine learning. It wasn't like AI or anything like that. It was just like some weights and some flow chart things. And all of that is sort of to say, like, I think that my definition of what AI is is not about like, oh, does this thing use machine learning? Because like 20, 30, 50 years ago, I don't even think machine learning was sort of not popular. Well, 50 years ago, I suppose it was like on the tail end. But there was like a while of machine learning being kind of popular. And then there was the Light Hill report that was sort of like, we've wasted a lot of money on this and it's not paying off, and like these people don't actually know what they're doing. And then there was the AI winter, and then I think through the 90s, there was like a lot of like expert system encoding and stuff like that. Nothing machine learning related. It was not AI as we know it today. But I think the thing that we would all recognize all the way through, continuously, like is the techno-political project of taking decisions away from people and putting consequential, life-changing decisions into a locus of power that is silicon or that is automated or something along those lines, and redistributing or shifting and allocating power away from collective and social systems and into technological or technocratic ones. And so this isn't really like a definition of AI that I think a lot of computer science people would appreciate or agree with. But I think it's the only one that, again, if you were a time traveler, kind of like going back 20 years and then 20 more years and then 20 more years, you would see totally different methods, but I think you would see basically the same goals, basically the same project. And I think that that's sort of like my definition of like what AI is. But I I'm kind of curious, like you've been talking to like tons of more people, way more people, way more variety of people. And I'm really curious, like, what sense do you have of like how you would define what AI is? Or does that definition cling in a bad way or like resonate strongly in a positive way? Or what are your thoughts?
SPEAKER_00I don't I don't know if I have a particular definition of like what AI is, because I feel like when I think about AI, I think like, oh, it's a marketing term for a bunch of tech companies to justify whatever they're doing and to like abstract what they're actually doing, you know, because artificial intelligence gives a particular idea of what is happening there. Um, and instead of using a term like machine learning or whatever else, um, you know, it's this abstract term that they can kind of fit anything under to justify what they're doing and give it this like air and mystique of uh, you know, something we should be interested in, right?
SPEAKER_01Yeah, Meredith Whitaker, I think, has talked about like the marketingness of uh the term AI. And I totally resonate with that. I think that's like sort of the beginning and the end of the analysis, right? But I also think like part of me is trying to figure out like what is the refracting of the light that they're trying to do with this lens, with this like weird marketing term. And maybe this is like me getting too caught up in trying to like understand a cynical ploy, but I think part of me wants to kind of understand like what is the what is the goal that they're trying to pursue here with calling things AI, with talking about a future of AI and things like that. So yeah, I think I've been like sort of fixated on, I mean, I've been thinking about this for a while. In particular, like whenever I submitted a paper back when I was publishing at Kai um at like HCI conferences and academic like places and stuff, um, I would talk about AI and then I would get notes that were sort of like, what do you mean exactly? And this and that. And I think like I was sort of nibbling around the edges, but then eventually I would be like, well, I'll just change it to algorithmic systems because I don't really want to have this fight. Uh but I I think it is a thing that I think a lot about. And it occurs to me that a lot of tech tech booster people will be very fuzzy and nebulous when they talk about AI, but then they get very like strict and like uh annoying and nitpicky and uh pedantic when they hear somebody critiquing it. And so I want to have something that like I can just deploy quickly and be like, here's a link. Uh like this is the definition, this is my operating definition. I don't care if you have another definition, but like this is kind of the most kind of comprehensive or encapsulating thing. And then I think the other thought is like maybe this is my computer science brain, but I also realize like I don't want to use a term that is indexical with another term. If I'm talking about AI and I really just mean algorithmic systems, I'd rather just say algorithmic systems. Or if I'm talking about AI and I really just mean machine learning, I'd really just rather say machine learning. And I don't know what AI is providing to my analysis if I use that term when I could use a specific other term and capture the exact same things that I'm talking about. But I do know that the word or the term or whatever has a kind of meaning. It captures some overlaps of some ideas and excludes some other things. I don't know to what extent people would really agree with this, but I think that like, for instance, I know this is like background info, but like I have a cousin who's a researcher at a university who does like machine learning stuff, but it's like protein folding and stuff. Like, I don't think he describes his work as AI. I think he talks about it as machine learning. And I think that like it's about as close to AI as anything gets, uh, unless your definition is about the techno-political kind of the power system, that power structure. When you include that definition, then it becomes clear why he doesn't talk about it as AI. And it becomes a lot less confusing why some things that are machine learning are AI and some things that aren't are still called AI and everything else like that. And then also why some things that are just like a flowchart behind a curtain at Stanford are called AI or called algorithmic or whatever, when really only by the loosest possible definition do they do they qualify by that. Uh and trying to understand like what is the kind of collective meaning that we're all building when people say that they're working with AI or that they're using AI or building an AI.
SPEAKER_00I think that makes perfect sense. And I appreciate you uh teasing it out. Ellie Al Khtib works with Logics magazine and was previously the director of the Center for Applied Data Ethics. Tech Won's Save Us if made in partnership with The Nation magazine, and is hosted by me, Paris Marks. Production is by Eric Wickham, and transcripts are by Bridget Pelou Fry. Tech Won't Save Us are lies in the support of listeners like you to keep providing critical perspectives on the tech industry. You can join hundreds of other supporters by going to patreon.com slash tech won't save us and making a pledge of your own. Thanks for listening. Make sure to come back next week.