How much would “owning” your data actually protect your privacy?

Host Kirsten Martin is joined by Ignacio Cofone, an assistant professor and Canada Research Chair in Artificial Intelligence Law & Data Governance at McGill University’s Faculty of Law. His research focuses on privacy harms and on algorithmic decision-making, with his current projects examining how to evaluate standing and compensation in privacy class actions and how to prevent algorithmic discrimination.

Ignacio came on the show to talk about his paper “Privacy Standing,” which appeared in the University of Illinois Law Review.

Providing courts with guidance on how to assess privacy injuries and advocating for people’s rights to seek compensation for them (i.e., legal standing), Ignacio’s paper distinguishes between what constitutes a privacy loss, a privacy harm, and an actionable privacy injury. He also seeks to define downstream, consequential harms as something distinct from privacy harms so that the latter can be recognized as harmful on their own and not dismissed simply because they haven’t (yet) led to something more tangible like identity theft or a financial loss.

As for where privacy harms originate, Ignacio emphasizes how frequently they arise not from the moment our data is collected but rather from the inferences later made about us from that data—or even from the data of others who just happen to be similar to us. That means the prevalent approach of giving people notice and choice—which Kirsten traces back to the economics of information literature of the 1960s—and its focus on asking users for permission to collect their data is in many ways inadequate when it comes to protecting our privacy.

Episode Links

• Paper Discussed in the Episode: “Privacy Standing”
• Ignacio’s Bio
• Episode Transcript

At the end of each episode, Kirsten asks for a recommendation about another scholar in tech ethics whose work our guest is particularly excited about. Ignacio highlighted three fellow law professors who also study privacy, among other issues:

• Salomé Viljoen (University of Michigan)*
• Rebecca Wexler (University of California, Berkeley)
• Margot Kaminski (University of Colorado Boulder)

*Salomé was also the guest for episode 10 of TEC Talks, “Moving Data Governance to the Forest From the Trees.”

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How could a personal characteristic like eye movement affect, say, whether you get a loan?

Host Kirsten Martin is joined by Sandra Wachter, a professor of technology and regulation at the Oxford Internet Institute (OII) at the University of Oxford. She founded and leads OII’s Governance of Emerging Technologies (GET) Research Programme that investigates legal, ethical, and technical aspects of AI, machine learning, and other emerging technologies.

Sandra came on the show to talk about her paper “The Theory of Artificial Immutability: Protecting Algorithmic Groups under Anti-Discrimination Law,” which is forthcoming in the Tulane Law Review.

Most people are familiar with the idea of anti-discrimination law and its focus on protected-class attributes—e.g., race, national origin, age, etc.—that represent something immutable about who we are as individuals and that, as Sandra explains, have been criteria humans have historically used to hold each other back.

She says that with algorithms, we’re now being placed in other groups that are also largely beyond our control but that can nevertheless impact our access to goods and services and things like whether we get hired for a job. These groups fall into two main categories: people who share non-protected attributes—say, what type of internet browser they use, how their retinas move, dog owners, etc.—and people who share characteristics that are significant to computers (e.g., clicking behavior) but for which we as humans have no social concept.

This leads to what Sandra calls “artificial immutability” in the attributes used to describe us, or the idea that there are things about ourselves we can’t change not because they were given by birth but because we’re unaware they’ve been assigned to us by an algorithm. She offers a definition of what constitutes an immutable trait and notes that there can be legitimate uses of them in decision-making, but that in those cases organizations need to be able to explain why they’re relevant.

Episode Links

• Paper Discussed in the Episode: “The Theory of Artificial Immutability: Protecting Algorithmic Groups under Anti-Discrimination Law”
• Sandra’s Bio
• Episode Transcript

At the end of each episode, Kirsten asks for a recommendation about another scholar in tech ethics whose work our guest is particularly excited about. Sandra highlighted University of Cambridge psychologist Amy Orben and her research on online harms, particularly in the context of young people’s use of social media.

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Host Kirsten Martin is joined by Ben Green, an assistant professor at the Gerald R. Ford School of Public Policy and a postdoctoral scholar in the Michigan Society of Fellows at the University of Michigan. Specializing in the social and political impacts of government algorithms, with a focus on algorithmic fairness, smart cities, and the criminal justice system, Ben is also an affiliate of the Berkman Klein Center for Internet & Society at Harvard University and a fellow of the Center for Democracy & Technology.

He came on the show to talk about his paper “Escaping the Impossibility of Fairness: From Formal to Substantive Algorithmic Fairness,” which recently appeared in Philosophy & Technology.

Ben begins by explaining the aforementioned “impossibility of fairness,” an idea that describes the incompatibility of different mathematical notions of what makes a system fair. By focusing on meeting one of these formal definitions of fairness, an algorithm that is mathematically “fair” can nevertheless yield decisions that re-entrench real-world injustices, including those it may have been designed to counter.

Asking whether the ultimate purpose of an algorithm is to satisfy a mathematical formalism or rather improve society, Ben puts forward an alternative notion of what he calls substantive algorithmic fairness—his detailed diagram of which, labelled Figure 2 in the paper, made a lasting impression on Kirsten. His approach still envisions a role for mathematical conceptions of fairness, but it repositions them as one consideration in a broader process where the primary concern is accounting for and mitigating both upstream inequalities that exist before an algorithm is deployed and downstream harms present afterwards.

Episode Links

• Paper Discussed in the Episode: “Escaping the Impossibility of Fairness: From Formal to Substantive Algorithmic Fairness” (Note: Figure 2 referenced in the episode appears on p. 17.)
• Ben’s Bio
• Episode Transcript

At the end of each episode, Kirsten asks for a recommendation about another scholar in tech ethics (or several) whose work our guest is particularly excited about. Ben highlighted four he says are working at the intersections of AI, ethics, race, and real-world social impact:

• Rashida Richardson (Northeastern University)
• Anna Lauren Hoffmann (University of Washington)
• Lily Hu (Yale University)
• Rodrigo Ochigame (Leiden University)

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Host Kirsten Martin is joined by Daniel Susser, an assistant professor in the College of Information Sciences and Technology and a research associate in the Rock Ethics Institute at Penn State University. A philosopher by training, he works at the intersection of technology, ethics, and policy, with his research currently focused on questions about privacy, online influence, and automated decision-making.

Daniel came on the show to talk about his short essay “Data and the Good?” that recently appeared in Surveillance & Society.

Considering the intersection of scholarship in privacy law and surveillance studies, he notes how research in these fields tends to focus on critiques of existing technologies and their potential harms. While he and Kirsten are quick to emphasize how necessary this kind of work is, Daniel describes his paper as a provocation meant to push researchers, himself included, to at the same time put forward substantive alternatives for how technology could or should be used. He says there are understandable reasons why this doesn’t happen more often, but that absent competing visions for our technological future, we are beholden to those crafted by the technology industry.

Episode Links

• Paper Discussed in the Episode: “Data and the Good?”
• Daniel’s Bio
• Episode Transcript

At the end of each episode, Kirsten asks for a recommendation about another scholar in tech ethics (or several) whose work our guest is particularly excited about.

In addition to citing classic texts in science and technology studies by Langdon Winner and Phil Agre as well as The Convivial Society blog, which applies classic writing in philosophy of technology to contemporary problems, Daniel highlighted three people working to advance alternative visions of technology: 

• Ruha Benjamin (Princeton University)
• Salomé Viljoen (University of Michigan)*
• James Muldoon (University of Exeter)

*Salomé was also the guest for episode 10 of TEC Talks, “Moving Data Governance to the Forest From the Trees.”

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Host Kirsten Martin is joined by Salomé Viljoen, an assistant professor of law at the University of Michigan Law School and an affiliate of the Berkman Klein Center for Internet & Society at Harvard University. She studies the information economy, particularly data about people and the automated systems it trains, and is interested in how information law structures inequality and how alternative legal arrangements might address that inequality.

Salomé came on the show to talk about her paper “A Relational Theory of Data Governance,” which appeared in The Yale Law Journal.

The paper proposes a new framework for thinking about how we govern the use of people’s data, so she and Kirsten begin by discussing the current/traditional approach focused on the privacy of individual transactions and the degree to which we consent to share our own information. However, Salomé explains what this approach misses, saying how in the digital economy, data isn’t collected to make decisions about any one person. Instead, it’s used to understand populations of people with similar interests, backgrounds, etc. and then predict things about them, such that opting out of sharing your own data doesn’t change the inferences being made about you.

Based on Salomé’s argument, Kirsten compares putting all our attention on the handoff of our data rather than on what happens with it afterwards to the old adage about missing the forest for the trees. Salomé then details what she means by moving toward a relational theory of data governance, one that accounts for population-level impacts of big data, recognizes both its potential benefits and harms, and prioritizes the scrutiny of data flows most likely to affect vulnerable communities in disproportionately negative ways (e.g., facial recognition data).

Episode Links

• Paper Discussed in the Episode: “A Relational Theory of Data Governance”
• Salomé’s Bio
• Episode Transcript

At the end of each episode, Kirsten asks for a recommendation about another scholar in tech ethics (or several) whose work our guest is particularly excited about. Salomé highlighted four:

• Beatriz Botero Arcila (Sciences Po)
• Ignacio Cofone (McGill University)
• Elettra Bietti (New York University and Cornell Tech)
• Amanda Parsons (University of Colorado Boulder)

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In this second of a two-part episode, host Kirsten Martin continues her conversation with Luke Stark, an assistant professor in the Faculty of Information and Media Studies at Western University in London, Ontario, and Jevan Hutson, an associate at Hintze Law PLLC. Luke researches the historical, social, and ethical impacts of computing and artificial intelligence technologies, and Jevan‘s practice focuses on the intersection of privacy, security, and data ethics.

They came on the show to talk about a paper they coauthored titled “Physiognomic Artificial Intelligence,” which appeared in the Fordham Intellectual Property, Media and Entertainment Law Journal.

In the first episode, Luke started with the troubling history of physiognomy and phrenology. These two pseudosciences were widely discredited in the early 20th century, but their notions that people’s external appearances can be a way to access internal truths about them have made a comeback in the form of AI systems that purport to be able to perform this type of analysis. Jevan also discussed some of the troubling commercial applications in areas like hiring, education, and criminal justice where we’re already seeing this “physiognomic AI” deployed.

Part 2 picks up with Kirsten asking Jevan about the menu of regulatory options he and Luke propose in the paper to remedy the fundamental problems with these systems. Jevan describes why they think physiognomic AI should be barred completely and the existing legal frameworks through which that might happen. Kirsten adds that the gap between AI ethicists and other technologists is larger in this area than just about any other, and Luke suggests computer vision isn’t the only field of study where physiognomic impulses can still be found.

Episode Links

• Paper Discussed in the Episode: “Physiognomic Artificial Intelligence”
• Luke’s Bio
• Jevan’s Bio
• Episode Transcript

At the end of each episode, Kirsten asks about another scholar in tech ethics (or several) whose work our guest is particularly excited about. Luke and Jevan highlighted three:

• Deb Raji (Mozilla Foundation)
• Catherine Stinson (Queen’s University)
• Jennifer Lee (ACLU of Washington)

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It’s a TEC Talks first: two guests! Host Kirsten Martin is joined by Luke Stark, an assistant professor in the Faculty of Information and Media Studies at Western University in London, Ontario, and Jevan Hutson, an associate at Hintze Law PLLC. Luke researches the historical, social, and ethical impacts of computing and artificial intelligence technologies, and Jevan‘s practice focuses on the intersection of privacy, security, and data ethics.

They came on the show to talk about a paper they coauthored titled “Physiognomic Artificial Intelligence,” which appeared in the Fordham Intellectual Property, Media and Entertainment Law Journal.

And with two guests, the conversation went a little longer than usual, so we’ve decided to break it into two parts.

In part 1, Luke starts with a quick overview of physiognomy and phrenology, two pseudosciences with racialized and gendered histories that claim people’s inner traits can be discerned from their physical/behavioral characteristics and the shapes of their skulls, respectively. Although physiognomy and phrenology were widely discredited in the early 20th century, the notion that external appearances can be a way to access internal truths has made a comeback in the form of AI systems that purport to be able to perform this type of analysis.

Jevan discusses some of the troubling commercial applications in areas like hiring, education, and criminal justice where we’re already seeing this “physiognomic AI” deployed. Luke also addresses why one human being making inferences about another—something we all engage in all the time with, as he points out, very mixed results—is fundamentally different from a computer trying to do the same. He says that this is simply beyond the capabilities of artificial intelligence, with Kirsten noting that because the flaw is in the concept of physiognomic AI itself, no amount of additional data will fix the problem.

Episode Links

• Paper Discussed in the Episode: “Physiognomic Artificial Intelligence”
• Luke’s Bio
• Jevan’s Bio
• Episode Transcript

At the end of each episode, Kirsten asks about another scholar in tech ethics (or several) whose work our guest is particularly excited about. However, because we split this conversation into two parts, you’ll have to come back September 7 for the second to get Luke’s and Jevan’s recommendations. Stay tuned. :)

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Host Kirsten Martin is joined by Ari Waldman, professor of law and computer science at Northeastern University, where he is the director of the Center for Law, Information, and Creativity. A leading authority on law, technology, and society, he studies how law and technology affect marginalized populations, with particular focus on privacy, misinformation, and the LGBTQ community.

Ari came on the show to talk about his book Industry Unbound: The Inside Story of Privacy, Data, and Corporate Power, published in 2021 by Cambridge University Press.

Intended for both a general audience of technology practitioners and more research-focused tech scholars, the book begins with interviews meant to construct a “day in the life” of people working at tech companies—which in one instance included something called “the bro meeting”—and their thoughts on privacy. Ari says his two biggest takeaways from a sociological perspective were the limits to these employees’ conceptions of what constitutes “privacy” and a false consciousness of what their companies were actually doing (or not doing) on that front.

He and Kirsten talk about how compliance is routinely used as a way to advance the goals of industry rather than the rights of users, with the corporate idea of privacy even shaping the regulatory approach of laws like Europe’s General Data Protection Regulation (GDPR), such that companies don’t have to change their underlying models of data extraction.

Kirsten and Ari also cover parallels between privacy and diversity compliance, the problems with notice and consent, how privacy shouldn’t be confused with encryption and security, the way siloed teams hinder information flow and negatively impact the product design process, and what it would take to shift the culture around privacy.

Episode Links

• Book Discussed in the Episode: Industry Unbound: The Inside Story of Privacy, Data, and Corporate Power
• Ari’s Bio
• Episode Transcript

At the end of each episode, Kirsten asks for a recommendation about another scholar in tech ethics (or several) whose work our guest is particularly excited about. Ari highlighted seven whose scholarship relates in one way or another to the issues he tackles in Industry Unbound:

• Lauren Edelman (UC Berkeley)
• Julie Cohen (Georgetown University)
• Salomé Viljoen (University of Michigan)
• Alicia Solow-Niederman (University of Iowa)
• Rashida Richardson (Northeastern University)
• Kate Weisburd (George Washington University)
• Matthew Tokson (University of Utah)

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