Kelly Hannah-Moffat, Algorithmic Adaptability and Ethics Washing: Appropriating the Critique (Ethics of AI in Context)

Ethics of AI in Context

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Algorithmic Adaptability and Ethics Washing: Appropriating the Critique

The emergence of artificial intelligence (AI) and, more specifically, machine learning analytics fuelled by big data, is altering some legal and criminal justice practices. Harnessing the abilities of AI creates new possibilities, but it also risks reproducing the status quo and further entrenching existing inequalities. The potential of these technologies has simultaneously enthused and alarmed scholars, advocates, and practitioners, many of whom have drawn attention to the ethical concerns associated with the widespread use of these technologies. In the face of sustained critiques, some companies have rebranded, positioning their AI technologies as more ethical, transparent, or accountable. However even if a technology is defensibly ‘ethical,’ its combination with pre-existing institutional logics and practices reinforces patterns of inequality. In this paper we focus on two examples, legal analytics and predictive policing, to explore how companies are mobilizing the language and logics of ethical algorithms to rebrand their technologies. We argue this rebranding is a form of ethics washing, which obfuscates the appropriateness and limitations of these technologies in particular contexts.

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This is an online event, available on the Centre for Ethics YouTube Channel, on Tuesday, February 1. Channel subscribers will receive a notification at the start. (For other events in the series, and to subscribe, visit YouTube.com/c/CentreforEthics.)

Kelly Hannah-Moffat
University of Toronto
Criminology & Sociolegal Studies and Sociology
Vice-President, People Strategy, Equity and Culture

 

 

co-sponsored by:
Centre for Criminology & Sociolegal Studies, University of Toronto

Tue, Feb 1, 2022
04:00 PM - 05:30 PM
Centre for Ethics, University of Toronto
200 Larkin