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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