▶ Video of Lecture

Avery Slater, The Golem and the Game of Automation

The Ethics of Remote Fieldwork in a Pandemic: Some Reflections on Performances in a Zoom Room

In this episode, Aden interviews Kristen Csenkey,  is a PhD in Global Governance Candidate at the Balsillie School of International Affairs through Wilfrid Laurier University in Waterloo. She is a Fellow with the Canadian Global Affairs Institute (CGAI). Kristen was the 2020 Women in Defence and Security (WiDS) Fellowship recipient and a Women in International Security (WIIS) Canada Emerging Thought Leader in Digital Security. You can find her on Twitter at https://twitter.com/KCsenkey, as well as some of her work on linktr.ee at: linktr.ee/KristenCsenkey.

Ethics of Research (Ep. 5): Kristen Csenkey

What’s the difference between merely being offensive and being unethical? When does a joke go too far? Open Questions is a production of the Centre for Ethics at the University of Toronto. The music is written and performed by Markku Wainman.

Why Is It Okay to Make Fun of Elites?

Data-driven decision-making regimes, in the form of predictive tools like crime hotspotting maps and risk assessment instruments, are rapidly proliferating across the criminal justice system as a means of addressing accusations of discriminatory and harmful practices by police and court officials. In recent times these data regimes have come under increased scrutiny, as critics point out the myriad ways that they can reproduce or even amplify pre-existing biases in the criminal justice system. These concerns have given rise to an influential community of researchers from both academia and industry who have formed a new regulatory science under the rubric of “fair, accountable, and transparent” algorithms, which seek to optimize accuracy and minimize bias in algorithmic decision making systems.

In this talk, Barabas argues that the ethical, political, and epistemological stakes of criminal justice applications of AI cannot be understood simply as a question of bias and accuracy. Nor can we measure the impact of these tools if key outcome measures are left unexamined. She outlines a more fundamental, abolitionist approach for excavating the ways that predictive tools reflect and reinforce the punitive practices that drive disparate outcomes in the criminal justice system. Finally, she will illustrate a more transformational approach to re-imagining the way data might be used to challenge the penal ideology and de-naturalize carceral state practices.

Chelsea Barabas
MIT
Media Lab

▶️ Video of Lecture

Chelsea Barabas, Beyond Accuracy and Bias: The Pursuit of “Ethical AI” in Criminal Law

Machine learning systems are implemented by all the big tech companies in everything from ad auctions to photo-tagging, and are supplementing or replacing human decision making in a host of more mundane, but possibly more consequential, areas like loans, bail, policing, and hiring. And we’ve already seen plenty of dangerous failures; from risk assessment tools systematically rating black arrestees as riskier than white ones, to hiring algorithms that learned to reject women. There’s a broad consensus across industry, academe, government, and civil society that there is a problem here, one that presents a deep challenge to core democratic values, but there is much debate over what kind of problem it is and how it might be solved. Taking a sociological approach to the current boom in ethical AI and machine learning initiatives that promise to save us from the machines, this talk explores how this problem becomes a problem, for whom, and with what solutions.

Video of Lecture

How Ethics of AI Became a Problem (feat. Daniel Greene)

A selection of interviews and talks exploring the normative dimensions of AI and related technologies in individual and public life, brought to you by the interdisciplinary Ethics of AI Lab at the Centre for Ethics, University of Toronto.

Chelsea Barabas, Beyond Accuracy and Bias: The Pursuit of “Ethical AI” in Criminal Law

Ethics of AI in Context

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