Shannon Hoff, A Phenomenological Account Of The Conditions Of Transnational Feminism

Jeta Mulaj, Balkanization And The Racial Order

Anthony Sangiuliano, Discrimination And Psychological Harm

Miko Zeldes-Roth, Escape From Politics: Personal Responsibility And White Citizenship In America

Yukiko Kobayashi Lui, Feminist And Queer Legal Theory Toward Family Abolition

Jerome Clarke, The Data Of Blackness

William Paris, Undisciplining Time?: Critique, Utopia, And The Prospects Of Life Beyond Capital

AI As Moral Patient

Anton Ford, The Objectification Of Agency

Lucien Ferguson, The Spirit Of Caste

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)

Coming to you from C4E, the Centre for Ethics, University of Toronto, and its Ethics of AI Lab ("Where Conversations About Ethics Happen"): conversations about ... ethics! 
Another podcast worth checking out: C4eRadio: Sounds of Ethics, our regularly updated collection of dozens of C4E lectures.

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

Let's Get Ethical

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