Anita Say Chan, "Predatory Data: Eugenics in Big Tech and Our Fight for an Independent Future" (U California Press, 2025)
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Welcome to the new Books Network.

Welcome to the NewBooks Network. I'm your host, Michael LaMagna. We often hear about the benefits of big data with the technology that we use today. These benefits are often cited as the efficiency and ease of use. However, the collection of data and the surveillance it brings can have negative implications. The truth is we've seen the negative implications between big data experts and the algorithmic discrimination during the late 19th and early 20th century.

Today, I'm joined by the author of Predatory Data, Eugenics, and Big Tech, and Our Fight for an Independent Future, published in 2025 by the University of California Press. Anita Seychan is an associate professor of information sciences and media studies at the University of Illinois Urbana-Champaign. Welcome to the podcast, Anita. Hi, thanks for having me.

So before we discuss your book, Predatory Data, I was hoping you could tell us a little bit about yourself, your background and your career path. Yeah, I work in a school of information sciences at the University of Illinois in Urbana-Champaign.

I was trained in science and technology studies and also have a cross appointment in media studies. I did my master's in media studies. I did both of my degrees, my master's degree and my doctorate at MIT.

in their social sciences and humanities units. So a really interesting place to get a humanities-based and critical social science-based degree, but one that I encourage many of your listeners to look into if they're similarly interested in sort of the social aspects of technology and datafication. Yeah, excellent. So let me ask you, what sparked your interest in exploring the issue of predatory data?

Well, as I mentioned, I teach in a school of information sciences. And before then, you know, I got my training in a discipline like science technology studies at the history and anthropology of science technology and society at MIT and was teaching in a media studies department before. So I've been surrounded primarily, you know, by humanists. And information sciences is a STEM based discipline and one where many of our grad

graduates, undergraduates, graduate students even imagine their career path is often taking them into STEM based careers, including graduating as data scientists to go on to work in data science fields in Silicon Valley and tech companies. So when my appointment from my university moved to information sciences, I was really shocked to see how little historical

understanding or awareness students and sometimes even some of my colleagues had about the origins of their discipline and really was shocked to see how little literacy my students had around, for instance, that the very sorts of

methods they're being trained in to practice data science the most fundamental method is statistical regression and that the kind of very practice of datafication all of that you know the and the practice of that at scale all of that had its origins in eugenics over a century ago that Sir Francis Galton a cousin of Charles Darwin was

was the originator of eugenics methodologies, uh, and that statistical regression was a method that he innovated and developed essentially out of his own race-based anxieties and his conviction that, uh, what he was seeing around him, uh,

That is the, you know, increasing in the 19th century, late 19th century, the kind of increasing globalization of Western cities and Western urban environments, the concentration on new proximity of classes, and a kind of mixing and mingling of

of heterogeneous classes that we'd never seen before, you know, post-independence movements going on globally, post a kind of new opportunity for freedom and mobility and movement across different, across the globe that we hadn't seen before, new patterns of immigration. For many people, of course, that opened up new prospects of hope, but for eugenicists, that inspired exactly the opposite.

kind of reaction. And Galton was part of that sort of elite patrician Western class that was dismayed. And rather than looking at this, these new patterns of global movement and heterogeneous mixing and proximity, he instead looked at it instead with deep paranoia.

And it inspired within him a kind of racialized paranoia and wanted his development then of statistical regression and of eugenics as a movement and his call for the widespread monitoring of populations, especially to be able to track degeneracy.

And biological degeneracy, something that he was convinced would start to spread into more elite classes and would eventually be the downfall of Western civilization that would bring about the degeneracy and the decay of.

of the sort of specialness of elite classes and again, the fall of the West because we were bringing in, because there was this new sort of heterogeneous mixing, he believed that the data would prove that and that we should therefore be monitoring human populations at scale and especially tracking for degeneracy

and controlling for that so that eventually policies like racial segregation, class-based segregation, even immigration exclusion laws, and later down the line, sterilization laws could all be enacted supposedly for the benefit and for the survival of the fit to enable for some version of eugenic utopia to emerge.

But none of my students were aware of that kind of history. And many of them even weren't familiar with eugenics or if they had heard about it, had only heard about it as sort of a something associated with the Nazi regime and that supposedly had already gone away. And so the notion that it's still with us.

that had never really gone away and that it was in the West, especially in the decades well before the rise of Nazi Germany, where some of the most robust and at scale widespread sort of expansive

gains of the eugenics movement really took their hold. Thank you for that. That's a very interesting history of eugenics, not only in the United States, but globally. And we can see how this movement incorporated research and data into what they were doing. Now, you highlight the Futurama Showcase commissioned by General Motors Inc.,

for the 1939 World's Fair. And you discussed that this includes predictive flow, automated highways, driverless cars, and a planned suburban community. And can you describe how the showcase and how it connects to the idea of maximizing efficiency

while removing inefficient drag, kind of a theme that we're seeing play out today. Yeah. Well, the Futurama was very much one. I mean, so the World's Fairs exhibits were really very much ones that were meant to sort of model future societies. They were really sort of meant to be sort of showcases of innovation for American audiences and

and were ones that invited literally millions of people to come in and to get a taste, a sample of what engineering in the US, what design and industrial design was producing, a glimpse at new future societies.

And oftentimes also, I think, as many sort of historians of world fairs in the U.S. are well familiar with, also showcased and performed and created a theater, a quite racialized and racist theater that demonstrated to or that was meant to sort of empirically demonstrate the backwardness of

of non-Western societies and traditional cultures, sort of setting up a kind of theatrical contrast between the sort of wild and futuristic innovations

of the U.S. and the West and the sort of regression and backwardness of non-white populations. And so the World's Fairs were very much sort of, you know, again, sort of the showcase, a sort of temporal showcase and oftentimes a theater that made an argument around

the kind of evolution of civilization, quite racialized. And that sort of led to, as its pinnacle, Western and a white-dominated kind of products of engineering culture

in the name of Western civilization. And by 1939, the World's Fair had also sort of erected as one of its kind of what was really the showcase, the kind of the star of the World's Fair was an exhibit that was known as the Futurama. This is a 1939 World's Fair exhibit.

that was hosted in New York City. And it was an exhibit that was commissioned by General Motors. And for folks who aren't familiar with it, I mean, one of the super interesting things that immediately grabs people is just how already the Futurama is this kind of big exhibit. It was immersive, like you could come inside essentially. And it was designed almost like a ride where for essentially individuals

entrance would come in, would line up, and then be sort of taken through in this sort of like ride, like take a seat in the ride and be taken alongside this exhibit that modeled a kind of miniature

future city, again, all sort of erected by General Motors, commissioned by General Motors. And so the point of it really was for viewers to get to sort of look over and look at the sort of marvel of what this sort of new future city and the marvels of new future modes of transportation are.

that this kind of streamlined future city exhibited. It was one where, so it modeled a couple of different things. Immediately for contemporary viewers, readers, audiences, one of the things that grabs people about what the 1939 World Fair was modeling was that it already had the notion of driverless cars, right?

And so this sort of the exhibit itself, you know, would be this miniature exhibit that was set up and designed across a stretch. I mean, it was something like the entire exhibit was something like over a mile long. And so what what what writers inside of the Futurama would see was essentially get a kind of God's eye view of

of this miniature city and environment that again was designed and all meant to sort of showcase the principles of what design innovation at the time was calling streamlined design. So taking away all manner of friction and excess that was really a kind of design principle that was inspired by eugenics, notions of excess and noise and taking away all kinds of polluting elements

So the kind of utopia that they were looking at was not one that just put those principles into design application, but also as they were looking over this sort of model future city model.

was one where they can already see, you know, sort of where viewers were meant to sort of marvel at the kind of bucolicness, the peacefulness, the sort of absence of conflict that was all produced through the kind of application of streamlining design principles and importantly, through the notion that an engineer was behind the kind of design and the running and management of the city. So,

So the idea of frictionless of driverless cars, for instance, was all all put in place under the notion that there the traffic would be controlled from a control tower by an engineer who managed to keep traffic flowing efficiently without any kind of.

of traffic, you know, the sort of chaos and noise that human run systems, independent human run systems would create would all be solved for through this kind of godlike role of the engineer.

uh, and not coincidentally, any version of sort of, um, heterogeneous, um, racially mixed, diverse, um, and, uh, inclusive societies that we might imagine in any modern day city, um, was all absent from, um, from this. Um, for the, by and large, the, um, the viewers who were, um, who were, uh,

documented and showcased from GM's own publicity around the Futurama were themselves only white viewers and the exhibit itself only had, of course, what were described as fit bodies

and nuclear families who were imagined as the kind of utopic users of this essentially eugenic society that was being modeled as part of the future. The designers of the Futurama were, as I mentioned, part of this movement called Streamline Design. So Norman Bel Geddes was the famed designer.

of the Futurama. But he had other compatriots as well who were part of the streamlined design movement. And they were, again, as I mentioned, ones who were real advocates of thinking in an age, in the mid-1990s,

In the mid-20th century, in an age that, or in the decades following, you know, the sort of, let's say, growing public awareness and the pushback on the kind of appall of what the Nazi regime had done,

had infrastructured and had executed eugenics. The kind of standard history of eugenics is that after sort of World War II, you know, eugenics sort of fell out of popularity. And we forget how deeply popular it was in the late 19th century in the U.S. and the 20th century, which I can get back to as well. But since we're on the streamlining chapter, we're already sort of in the 1930s and edging into the middle part of the 20th century.

The work of this chapter is to really demonstrate just how the continuity of eugenics and eugenics really finding the marketplace as a new realm to be able to exhibit and argue for its principles. That is getting rid that getting rid of the excess of the inefficient of polluting and smelly elements as streamliner streamlined designers would literally write about.

in their advocacy for what were the benefits, what were, what was the sort of merit of streamlined design? And what were successes accounted, how were successes accounted for? They would credit streamlined designs, aesthetic beauty, but also it's sort of market efficiency for getting rid of all those excessive elements. Raymond Lowy was another sort of well-known

streamlined designer who became a big advocate and was well celebrated even still within industrial design circles for his advocacy, especially in his innovation of streamlined designs. You know, things like the bullet trains, these sort of bullet trains and these smooth,

Kind of long movement oriented, you know, getting rid of any sort of material build outs that would add friction.

to a kind of, to a train or to a moving transportation vehicle. Loewy and his streamlined designers, Norman Belgedy included, were part of that sort of movement for

this kind of modernist turn. But Loewy especially was a big advocate for sort of thinking through exactly the way their streamlined design paralleled the principles of eugenics overall. Again, sort of getting rid of excessive, degenerating elements in design and in aesthetics, and in the way that both improved the physical beauty of an object, and then also improved the economic efficiency and the production of that object at scale.

and therefore was introducing more efficiency into capitalist markets and that streamlining the principles of eugenics could translate and not just sort of policy, the kinds of policies that I was mentioning before, immigration exclusions, remaking populations through national populations, through immigration exclusions, or allow the forced sterilization process

Of the unfit, which had passed this law in over 330 U.S. states by the 1930s. So beyond just sort of those ways to remake human populations as under a kind of eugenic.

model, you might also start to apply the principles of eugenics into the marketplace and that this too would introduce all kinds of new forms of marketplace aestheticizations, beautifications, but also new economic efficiencies. And of course, we're seeing those kinds of arguments now also put in place and revived with the kinds of enactments that the Trump administration has very quickly put in place in its opening months.

Thank you.

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So we could definitely see that connection between the streamlined design, the eugenics movement, how in the early part of the 20th century, it really is market driven. And you make that excellent connection to what's happening today. So how does this connect to our current belief of convenience through technology and data? Well, it's been so the notion this I write about eugenics is really the first global datification movement.

and also the first, alongside with it, also the first global disinformation movement. So we've seen, I mean, nowadays, you know, the notion of a hyper-extractive and voracious kind of social media environment that has called upon the constant surveillance, the hyper-surveillance, but also the at-scale, real-time, constant experimentation of

on human populations. That is what we have seen play out across the last decade and more in terms of what social media systems, online platforms have done to audiences and now moving into sort of more predictive platform ecologies where these sorts of models of voracious datification and predatory datification

That, again, you know, are being used to profile of profile citizens and users constantly have been used to turn against them, oftentimes to create predictive models to help supposedly employers or other institutions, whether it's governments in the states, identify everyone from, let's say, who's the best hire.

to who should stay in jail, who should not based on likelihood for recidivism to go back to jail. And there are systems, real world systems like Compass that are actually used by even U.S. judges in various states to be able to help with prediction, again, to figure out whether or not the case they're seeing before them is likely based on

a datafied prediction likely to see the case they're seeing before them is likely to have that individual repeat a crime or not. And so helped him to determine jail sentencing. Although the system has already has been assessed and critiqued actually by critical data practitioners and by data journalists, folks, some of your readers might be familiar

with some of the investigative journalism around the compass system that demonstrated that there was a racialized bias to how the system would predict which people would exercise, where there would be recidivism and that African-American

internees and were predicted to be, were over predicted to be recidivists, that is to repeat a crime that would warrant therefore a longer jail sentence than white internees in jails. And people can look at the ProPublica reporting of this.

of the system in the last several years to sort of look at exactly that kind of critical pushback. But see systems around prediction, around the datafied profiling of users at scale that are then turned against, used to sort of model and create sort of negative profiles of users

even cheaters for test takers, for what would someone who would look like a bad employee or a bad hire look like, right? Sort of, again, then creating these negative profiles so that you can create predictive systems and real tools and products that employers use, that universities have used, essentially to predict who is taking a test, who looks like they're cheating and who's not.

Or when universities are assessing applicants for new freshman classes, deciding who looks like they would be a successful student and who isn't to help with those sorts of assessments. Or similarly for hiring, decide again who would be a better employee and who not.

So all of these systems are based upon a version of monitoring individuals at scale, creating profiles to create a norm for what would someone who looks like they're doing something within a kind of wanted category and also not. But although these systems have been shown to fail,

Even on my own university campus at the University of Illinois, a monitoring, a test taking monitoring system called Proctorio, which was widely reviled by students even before at the University of Illinois, we canceled a contract with Proctorio.

Wildly reviled so that on any number of different Reddit boards for University of Illinois, University of Michigan, you can see dedicated boards and discussions filled with student complaints about how these facial recognition systems and monitoring systems work, how well they and how oftentimes they fail. And at the University of Illinois, it was especially students with disabilities who

who were complaining about the use of these systems and how they were being consistently overflagged for cheating based on facial recognition patterns that looked outside of the norm. Students of color were also experiencing these kinds of heightened flags. And so these datification systems, based on their creations of norms and their prediction for what would look normal and abnormal, was over and over again taxing

a body of users, but especially minoritized bodies of users to the point where the University of Illinois eventually canceled its contract with Proctorio. But this practice of hybridization

heightened datification and a kind of call to especially try to create models to predict what unwanted or unfit kind of behavior look like really all starts a century ago with the datification practices of eugenicists. So it's why, you know, again, the book is reminding, especially students in data science and in data practice, the long history of eugenics that Francis Galton,

And his acolytes from the beginning were calling for the need to massify data collection on populations at scale, but again, especially to monitor and surveil the unfit to try and create then to be able to create and design better policies and strengthen policies to segregate the fit from the unfit.

And Galton himself was obsessed with any version, any number of different kinds of counting. He was a cousin, as I mentioned, of Charles Darwin, and so was very much in his younger years thought of as someone who was or written about as someone who was sort of trying to find his footing in order to kind of compete with

the kind of success and national profile, celebrated profile of his better known cousin. He was known to sort of, or written about and described as sort of being a little bit of a dilettante, dipping his toe in meteorology and in exploration, anthropological sciences before he sort of found his call in eugenics.

But he was also known at the time to call for, you know, obsessed, was obsessed with counting and was convinced that,

that counting everything was sort of the secret to perfecting everything from making the perfect cup of tea to figuring out how to beautify London. So he had written, for instance, about how, you know, you could quantify what the perfect cup of tea would be with a perfect number of stirs and the exact quotient of sugar in a cup of tea. And so too, he would carry on these sorts of,

arguments to creating a kind of beauty map or proposing a beauty map of London, where in order to create it, what he had done was designed an invisible counter, a handheld counter that he held in his pocket, in his coat pocket, and essentially had a little pinpricker so he could pinprick invisibly

As he stood on a street corner in various neighborhoods in London and counted, as he put it, the number of attractive women per neighborhood, a pretty creepy endeavor. And he must have known it as well. Otherwise, he would not have made his counter an invisible counter.

But he would compare well-off neighborhoods with poor neighborhoods, essentially to make an argument fitting his kind of eugenic model that the better off neighborhoods were where beauty better concentrated. He would make similar kinds of arguments overall. I mean, this is part of his sort of eugenic program.

To essentially argue that the well-born were concentrated, the families of the well-born concentrated talent and fitness and education.

genius and economic sort of productivity so that you could predict just from one sort of biological origins and your origins within the family of the fit that those offspring would be

more inclined for future economic success and future genius and future intellectual prowess and future talent, exhibition of talent in the arts and sciences, etc. And he also argued, of course, the

that in parallel, that the poor and the unfit were more likely to have higher concentrations of undesired characteristics. Everything from criminality to alcoholism to licentiousness, so proclivity to become a prostitute, laziness, all of these things, even dissent.

and restlessness, right? So the likelihood to become someone who might cause political conflict as an organizer. All of these things were supposedly concentrated and genetically determined and concentrated as they concentrated in the poor that you could determine then a kind of

a model for future prediction that all of those characteristics, if you allowed the poor to expand and allowed the poor to sort of spread their genetic seed, that you would essentially allow for an increased volume of unfitness in national populations. And this would eventually be a formula for economic and national degeneration.

So all of these sorts of this kind of, again, this drive for quantification and surveilling of populations, but especially surveilling of minoritized populations were for eugenicists part of a formula of, as they saw it,

and improving society. And they were appalled that democratic institutions and the welfare state were doing exactly the opposite. So part of the program was to also make forceful arguments and to really attack democratic institutions by demonstrating the wastefulness and

the wrongheadedness of their program to invest in the poor and to invest in welfare policies that, as they saw it, only weakened

human populations and national populations and set us up for failure. How, in other words, could we possibly be investing in things in welfare policies that essentially allowed the seeds of the unfit to spread further, that allowed the children of the unfit to be treated equally as the children of the fit, and that were essentially stigmatized

stealing resources away from where they should be placed, i.e. that we should only be, states and governments should only be investing in the well-off and in the fit populations in order to strengthen national economies and to eventually realize, as Galton would put it,

what a universe of geniuses and talent we would see if we actually had states, rather than investing and wasting resources in democratic protections and democratic institutions built around the supposed equality of humankind. That is exactly the opposite vision of eugenicists. Eugenicists very much believed in societies as fundamentally unequal,

genetically unequal and that there could be a scientific program to demonstrate that in inequality, eugenics essentially as a science of prediction for inequality and a science fundamentally of inequality.

or a pseudoscience, we might say, although it was certainly treated as a science then. And we could see a lot of this playing out today. And so if you think about it, part of the eugenics movement in the early 20th century was this idea of merit, right? A merit-based system. So you've kind of touched on it, but how does this connect to our current thinking and the thinking of those in Silicon Valley and the technology class and those elites?

Oh, so the arguments around merit that we've seen over and over again come up are supposedly are sort of based around the kind of specialness of, of course, and the empirical specialness, supposedly the kind of empirical specialness of merit.

Of the elite and especially the techno and the scientific elite. One should keep in mind that this notion of merit and and the merit certified populations. Again, we can all trace back to eugenics and the notion that there were some populations that were more meriting of rights and privileges and investment than other populations that the poor people.

were essentially undeserving overall as a whole class. And that, again, democratic institutions were wasting resources on the undeserving and the unfit. Eugenicists were already sort of trying to make arguments, especially around intellect, to sort of demonstrate. I mean, they were obsessed with every version of fitness and they sort of categorized it as mental, moral,

uh and physical uh unfitness uh and uh um an important sort of thing to keep in mind as well is just how far and how wide um eugenics again as a movement had um had had taken off and how esteemed it was it really was if you looked at who were eugenicists that were who's who's um

of Western societies. American eugenicists were, you know, founders, the first founding president, not just cousins of Charles Darwin in the UK, who were granted their own labs at University College London, you know, an esteemed institution of higher education and university training.

But in the U.S., were the founding presidents of Stanford, David Starr Jordan, a very, very vociferous advocate of eugenics, were deans of Harvard Medical School, Oliver Wendell Holmes, were Supreme Court judges, Oliver Wendell Holmes Jr., who presided over the 1927 Buck versus Bell case that kept the legal for sterilization, the legal status of that in over 30 U.S. states. Right.

All of that was kept in place by the kind of work of a scientific elite and a research elite and a medical elite who were also heavy proponents of eugenics at the turn of the century. They were also ones who had very successfully managed to remake immigration laws so that the very first exclusion laws that we saw anywhere in the world were

were exercised as pro-eugenic arguments in the US and that first impacted with the 1875 Page Act, first impacted Chinese women,

not Chinese male laborers, but the Chinese Exclusion Act that first excluded Chinese women supposedly for their contaminating elements that they would release through being Chinese prostitutes. The notion being that almost all Chinese women who were allowed to come in at the time were supposedly all Chinese prostitutes or so disproportionately so that it put white populations at a risk.

And in this case, arguing that they would be a seed for contamination of white families through bad blood that that Chinese woman especially would let in that would then breed licentiousness, criminality, drug addiction with opium, etc. All of these sorts of degrading elements into white heteronormative and essentially good families.

domestic families. But the question of merit is one that by the early 20th century, eugenicists had turned from not simply advocating for

For immigration restrictions, they had managed to win that with the 1875 and 1888 Chinese exclusion laws. And then by 1917 and 1924, had put in our first immigration quotas in the US that targeted especially Chinese.

Exclusions for Jewish populations, Southern Europeans, Eastern Europeans and political dissenters were also allowed to be excluded or to be turned away and even deported under the 1917 and 1924 immigrant laws.

and new immigration laws that had been lobbied for by American eugenic, um, eugenicists, um, and led, um, uh, with the American records office, um, uh, um, leading the charge. Um, uh, um, but they had turned, um, by the early part of the 20th century, um, and really grown into a quite full fledged movement. And we're now, um, arguing for other kinds of policy changes, um, um,

beyond the kinds of immigrant exclusion laws that they had managed to get put in place in the 19th century. And so things like IQ tests and IQ exams as means to be able to not just stratify exercise, to not just stratify. Eczema isn't always obvious, but it's real. And so is the relief from EBLIS.

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student populations and to figure out who were the who were the mentally unfit and who were the gifted populations, but also applying them on immigrants at at Ellis Island and at ports of entry, essentially, again, to kind of create these

channels to only allow fit populations in and privilege Nordic classes and what the what eugenic arguments and and also the 1917 and 1924 Immigration Acts would term as as eugenic as as Nordic populations, that is, northern Europeans and

So those were all successful gains, and much of that had been done through advocacy around supposedly immigrants and non-Nordic population, immigrants especially from non-Nordic countries, demonstrating as the innovator, the pro-eugenic psychologist and innovator of IQ exams in the U.S., Henry Goddard, argued that non-Nordic immigrants were demonstrating

through his application of this, of his exam, an 80% profile, a ridiculous number, but that's what his publications based on his surveys resulted in, an 80% demonstration profile

Of feeble mindedness and that kind of advocacy was, again, what helped to get our new immigration exclusion laws put in place by the early decades of the 20th century. We should keep in mind, though, and this gets back to your question about the ties in not just the arguments of merit, but to the very the fact that it's always based.

on a version of information manipulation and disinformation. Even back in the day,

Even with Goddard's use of IQ exams, it was very, very clear that his exam was designed to be almost impossible to do well, even for a native English-speaking, U.S.-born resident who had lived in the country all their life. There were questions on the exam. The exam was entirely administered in English to new arrivals coming in from Southern Europe, from Latin America, from everywhere.

And included questions like, what is Crisco?

a US cooking product that I think many of my undergraduates have no idea what it is. So it was that shortening based alternative to butter that had only been introduced into the US marketplace a couple of years earlier. So even many Americans might not have been familiar with it. And who is Christy Mathewson, who is an American US football player, which again, many Americans would not have known simply because they may not have been fans of football.

So the question was very the questionnaire. The survey was very much designed to be one that immigrants and new arrivals would fail. And the numbers that he got, again, these these exaggerated numbers of 80 percent are feeble minded based on his administration of the test.

was completely doctored. It demonstrates a kind of continuity of eugenicists of the time continuing to fake the data, doctor the data, remake the data. A famous publication, oh well, there was also at the turn of the century, eugenics was

Also, as I mentioned, it was not by any means a kind of fringe discourse. It was something that eugenic leaders had really, especially by the beginning decades of the 20th century, turned into a kind of a mainstream vanguard idea. Many historians talk about the early decades of the 20th century in the U.S. as a golden age of

of eugenics publishing. And one of the best-selling texts of the time was a text on the Kalanick family, which was an Appalachian family that was traced through multiple generations. And to demonstrate their unfitness,

uh, uh, included, you know, this notion that feeble mindedness carried over genetically and that you could trace it through, um, poverty of, in this family, their, their continued poverty was a demonstration of, um, of their unfitness, not, not structural problems, not, um, structural discrimination or exclusions, uh, or, um,

of social policy, but in fact, the fault of the poor themselves. The Kalanick family, again, was this sort of publication that also included visual documentation where the photographs had been doctored in order to try and create abnormalities, physical, the appearance of physical abnormalities or just uglinesses. Again, to translate a kind of optics of unfitness and to translate the notion that there were some populations that merited physical

investment and others that didn't. And Silicon Valleyites today love to try and leverage their specialness as supposedly fitter engineers, as supposedly empirical. The fact that they are working in Silicon Valley

sort of is meant to certify their cognitive specialness, that they are of a higher IQ and that this would be traceable somehow through data and through some kind of true knowledge that could get literally quantified, that their value is quantified, their economic value, but their sort of intellectual and internal value as well. Again, using a kind of

eugenic framework. Um, and so when you hear, you know, Trump make, uh, and Musk, um, make these overtures, um, and literally draw from a eugenic language playbook, right. Talking about where con where genius and talent concentrates. This is literally language straight from Galton, right. Um, with, um, his, um, arguments around eugenics, uh, starting from the 1860s, um, 1860s, um,

throughout the rest of his career over and over again, making arguments that talent and genius concentrate in the families of the well-born. That language being newly repeated again by Trump and Elon Musk and their acolytes is no accident. These are directly drawn from a eugenics playbook and they try and attempt

to underscore for the public that there is greater merit in these populations, in the engineers of Silicon Valley, in the employees of Musk and Trump, in the kind of doge actors and in the teams who are there to adjudicate

what inefficiency looks like for government and what waste looks like within the American population, their merit and their special exceptionality supposedly authorizes them because they have no other form of authorization. But that

That kind of specialness and that merit supposedly is what merits their outside, is what accounts for and should enable and authorize their outsized and really unconstitutional kind of enactments of power and remaking of constitutionally protected vectors of government.

And we can see these themes playing out today and the language that's being used, even the golden age, right? So thinking about it, let's move to the positive side. So how can we move forward and move away from these predatory data practices to something that's more inclusive? And how does AI fit into that?

Well, I'll start with just something that's a little bit maybe not just, you know, sort of getting us to think about AI, but really to think about thatification.

and data and what a kind of different kind of knowledge economy would look like. And to remind ourselves that, you know, so just as predatory datafication has been with us for a long time, other forms of justice-based datafication have been with us for a long time too. And I give us the example of an infrastructure in Chicago called the Hull House, which was a network of researchers run by researchers

Ellen Gate Starr and Jane Adams, two famed feminists of the turn of the century who had founded Hull House in Chicago's 19th Ward, which was a large immigrant settlement district, certainly a neighborhood with a lot of poverty.

and under service by government institutions, exactly the place where new immigrants were settling in and also where a whole kind of economy of sweatshops had also burdened up. And Ellen Gate Star and Jane Addams' proposition

was to create as part of the settlement house movement. There were hundreds of settlement houses across the United States at the time that had grown essentially as these sort of alternative communities. And in the case of Hull House, alternative research community that was meant to sort of live alongside neighbors who

who were coming from marginalized backgrounds to really attempt to understand and create a model for understanding what poverty looked like. This is one of the obsessions of sociology and social sciences, of course, at the turn of the century, what were the origins of poverty? And social Darwinists and eugenicists, of course, had an answer.

that, as I mentioned, in the U.S. had gained quite a bit of traction with, again, not just in the marketplace of ideas, but real gains in terms of immigration policy, sterilization policy, and means of attempting to segregate, again, classes from one another and populations from one another that we still saw in place at the turn of the century.

So but Hull House had a different kind of proposition around what a research community could look like. And it was descended from the academy. And they had a different response for what the origins of poverty were. Unlike eugenicists and and social Darwinists, you know, they didn't see this as as about individual failing or about genetic failing. Right. That it was your own your own genetic profile.

or your own individual bad choices or individual moral failure, right? Again, your sort of, your penchant for criminality, for laziness, for alcohol or drugs, all of these things were what accounted for your, and what was to blame for your poverty.

and for your bad position in life. The Hull House researchers were really instead invested in looking at how structural organizations and power were really what was accounting for marginalization, discriminations, and forms of oppression were really what were to account for. The kinds of economic exclusions repeatedly that we saw with the families of the poor and

and with immigrants. And so the Hull House researchers did things largely disproportionately women and even some immigrant researchers who published texts like the Hull House maps and papers. And when you look at publications like that, they're full of data visualization. So it's super interesting for my students to take a look at to recognize that data visualization was not just a practice that emerged out of our internet economy in the last several decades, but it's really sort of a practice that stretches back

And with the kinds of data vacations that Hull House were up to, they were interested again in looking at the correlations between economic exclusion and

and the wage patterns they could see, and they could absolutely see things were really trying to data fire, what did the sweatshop system look like? What were the kinds of correlations we would see around childhood deformities around and, and the workplace and the integration of children, work, immigrant children into workplaces as young as two and three,

What were the kinds of workplace diseases that women and men as well would have a more heightened likelihood to get when they were exposed to toxic chemicals in the workplace? What were the kinds of demands of labor that exceeded, you know, well above 40 hours a week? And

What were the kinds of conditions of life that would or would not end up producing the kinds of profiles of unhealth or health? And again, poverty with the driving down of wages that we would see time and time again concentrated into areas like the 19th Ward. So the Hull House researchers were extremely successful at being able to data-fy

precisely this and the source of low wages and source of poverty, again, is not being an unwillingness to work or a kind of higher likelihood to instead be idle or to fall into alcoholism, etc. The whole house researchers were exceptional at being able to trace that even families that were working around the clock,

children as young as two and three integrated into the workplace economy, even those households were falling under the poverty line and were forced to live in miserable conditions in tenement housing and in neighborhoods where garbage and sewage was very routinely not getting picked up. The publications of things like the Hull House maps and papers led to

Any number of different kinds of expansions around advocacy for the eight hour work workday and anti child labor laws, and they were eventually successful in getting those things passed through not just thatifying again, the kind of systematic structural oppression that was being imposed upon.

vulnerable populations like the immigrant families of the 19th Ward, but also through allowing the Hull House infrastructure to be meeting places for immigrant families, labor unions to meet alongside with Illinois legislature members, to allow the researchers of Hull House to share findings in these social settings with local government actors, etc.,

and union organizers. So it was a very, very productive space and model for thinking about the kind of sociality and the social life of alternative data practices and thinking more broadly into our kind of

predictive economy of AI, you know, I also write about Hull House Maps and Papers and Hull House itself as a model as really sort of being a space that was quite improbable. You know, you wouldn't have thought that at the turn of the century, it's even hard to imagine it for some of my students now, that it would be a small non-university based

research hub founded by women and co-run by women and immigrants in a poor neighborhood that would be the model for datification in social sciences. Hull House was extremely impactful, I would say, in the day. The Hull House maps and papers volume is routinely credited with seeding community surveys, with inspiring not just other settlement houses to do similar practices, you

Survey that came out of Philadelphia settlement houses again another kind of model that was inspired by this, but inspired social official academics and professional social scientists, and the kind of professionalization.

later of sociology, of disciplines like sociology and social work and their integration of methods like community surveys is all credited back to Hull House and its research practices and its innovation of the day. And none of this was yet standard practice methodologically in these new disciplines. And Hull House was, again, one of these spaces that experimented with it. But again, highly, highly unlikely.

likely a kind of improbable world or an example of what I argue about in the book as a kind of improbable world that we couldn't have imagined or couldn't have predicted would have emerged. Your snacking routine can get a little dull. Time for an Oikos remix or light and fit remix. Like a crunchy storm of sea salt praline pretzels, dark chocolate and butter toffee showering down into a smooth creamy yogurt.

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And I write about many of our of of our most sort of cherished values and especially things when we're thinking about social revolution and democratic new democratic gains.

as oftentimes emerging out of these kinds of improbabilities. It's something that I worry about as becoming less and less likely in a world where we're more and more reliant on predictive systems and AI. What AI works essentially through being able to predict for the most likely outcome, right?

Right. So when you're using chat GPT, it does this remarkably well in many in many scenarios. Right. It predicts that we are writing an email or writing a letter and and that this is the kind of language we might like to use in these sorts of formal modes of communication. And.

And then tries to predict what our next kind of word or what kind of response would sort of best suit this kind of use case scenario. And again, for a lot of white collar workplace or sometimes even university and academic kinds of writing styles, it works exceptionally well because there's so much data.

around precisely that. Any number of English language papers and publications have been used and news articles have been used to feed the model of ChatGPT. And so it's constantly predicting for what the best use case scenario is given that sort of, given who it's predicting. And again, it's predicting for a norm. So it's thinking about majority use cases and predicting for a majority based outcome around probability. So making probabilistic reads

for what those best case use scenarios are and who to you are as a user. What it doesn't do well is read for improbabilities or leave for space for improbabilities, right? So if you are in casual communication or trying to communicate in ways that have not

not been well datafied and therefore can't be well predicted. Lots of uses of slang from youth populations and even minoritized populations. The uses of language in many minority contexts, whether it's minoritized indigenous language or just because, again, you're part of a very specific kind of user, you know, audience and community that uses slang in a particular way or talks about

disease outcomes in a very different way than say like a medical professional population might. And we can imagine all kinds of different social settings and use case scenarios where that those realities exist and are extremely and extraordinarily productive. But they're not what AI systems do well.

even in scenarios where you might think, oh, this is, you know, there's plenty of data-fied material, let's say, in musical production. There's so much digital music or music that's now been digital and AI can now do things like predict what would be a likely kind of

and even compose songs that someone who's just sort of working like lo-fi music for Spotify. Great, what would that sound like? And again, really good at being able to generate content based on those sort of majority world and use case outcomes. But artists and musicians are already complaining at how these systems really encourage and start to homogenize content production towards that norm.

and are terrible at being able to, of course, really think improbabilistically, right? So it's really thinking outside of the box and starting to generate other forms of expression,

that don't gradually push us more and more towards a mean and towards some version of majoritarian monoculturing. And so that's really the kind of fear of, again, if it's when we think about so many different ruptures, eruptures, and modes of revolution making that came really at

as a surprise. You know, right now, my student, we have an innovation studies class, and we look at models of education reform on university campuses as one of these places and moments that innovated a lot of educational policy that we now live with as part of our mundane and everyday on the 21st century university campus, that gave students a variety of different rights

and modes of giving feedback on the quality of education, the kinds of classes,

they wanted to see more of or didn't like. All of that is very much the norms of all that that we now think of as, you know, quite every day on university campuses. But all of that is very much a credit to the kinds of demands and pushes for innovation reform that student bodies themselves were behind for articulating. But that generation of counterculture on campuses, right, from the 1960s was a hugely important

hugely improbabilistic outcome. The 1960s emerged out of the most conservative decade, the McCarthy era 1950s, at a time where, especially in the 1960s, university campuses were even more homogenous than they look like today. Yet by the 1960s, certainly by the 1970s and the Vietnam War, they were looked to as very much the vanguard and the future of where the moral pulse of America was heading.

Again, something that couldn't likely have been predicted. And again, that you would see this kind of rupture at a time coming out of the most conservative, paranoid, McCarthy era driven kind of decade. But yes, how to preserve improbable worlds is,

And to really account for the work that they have done over the long course of history to push it for innovation in culture across time is one of the questions I want to leave us with.

Excellent. Well, Anita, I've taken up a lot of your time today. And as we wrap up our conversation, I was wondering, now that you've finished the book, what are you working on next? Oh, I am largely still working, I think, on various principles that surround the book. I have a

A lab at our university campus called the Community Data Clinic, and it puts into action many of the principles around local world making and alternative modes of that data collection and practice in collaboration with local social service organizations and community groups. And we have any number of ongoing projects.

collaborations with local organizations from our local CU PhD, our public health district and department, to the housing authority, to the Cunningham Township supervisor's office. A shout out to all of those organizations for not just doing the work that they do, but especially really demonstrating and standing up for

other forms of data use and practice and for keeping us in their circuit of research collaborators.

Those sound like excellent projects and something that your students are definitely going to benefit from. Well, Anita, I want to thank you so much for your time today. Can I mention one other thing, Michael, that I think, um, you know, is, um, it's not a part of the, the, um, the, um, data clinics work, but I do think that it's something a lot of people have been asking me in these conversations around the book, like what can they do? Um,

And I just wanted to give maybe two quick responses to that that I hope might be helpful for your audience. Again, around this kind of practice of datification. One of them is to certainly take a look and pay attention to sort of local anti-surveillance bills that have been popping up all around.

all over the country. Urbana-Champaign, where I live, has its own that's currently being debated by our local city council. It's a great bill that essentially just starts to create some stopgaps, some public oversight over local authorities' acquisition of surveillance technologies. Again, it doesn't stop

wholesale or ban surveillance technologies, but essentially obligates a kind of public conversation and public accounting for why public resources would be used to expand the use and the purchasing of the systems, how they would be used, etc., and if they shouldn't be used at all.

Giving an opportunity for the public to weigh in exactly on those kinds of conversations. So one is that I would encourage people to sort of look at those different movements that are happening because they're cropping up all over the country, especially right now. The other thing is, you know, that you too can get involved in some version of a positive datafication trail.

Right now, we're seeing any number of different institutions, including higher ed, essentially following attacks that were happening just this week on

on immigrant graduate students and graduate students with green cards, all legal residents to continue to study here, but students essentially at higher ed institutions and higher ed especially, all being attacked supposedly for versions of unlawful speech, although it is entirely protected

First Amendment rights, but threatening to either cut off funding or to deport students themselves. This is appalling. And, you know,

you know, there are ways to be able to, in a moment where many, um, leaders of, of democratic institutions, um, you know, many of them are sort of in this space of not knowing how to respond, um, remind your institutions, create these positive datafication trails, why you believe in your institutions, why you are an alumni of X, Y, or Z, um, university, uh,

And while you're proud to hold on to that degree, what it is, what academic freedom is,

or simply the model of pluralism that universities have stood for and have been in many ways the kind of highest emblems of in the United States, right? That they have managed to blend scientific advancement and models for pluralism, not always doing it perfectly, but on the whole, so many of their alumni and students are really proud of

Fight for the means to be able to attend those campuses, to be able to save enough to go to them. And when they graduate, are proud holders of those degrees. Why? Please create some, you know, trying to create some positive data records and writing to campus presidents and boards of trustees to say why it is you believe in these degrees and what you hope they stand up for.

It will embolden them. It should embolden them because the numbers are entirely on our side. It should embolden them.

To be able to stand up for the right thing rather than and defend their students and their faculty and overall just free speech as an academic independence and integrity as a kind of as necessary and important in order to keep America as the kind of the nation as strong and economically productive as it has been.

and politically aspirational as it has been for so many, they need to be able to stand up for that. And I hope that creating those positive ratification trails could actually be one mode of, again, being able to register where we would love for those defenses to go. Well, Nita, thank you so much for sharing those suggestions. And I think everybody's going to take them to heart. Again, I want to thank you so much for joining us today.

Thank you, Michael. I'm your host, Michael Magnet, and thank you for listening to the New Books Network. Thank you.