80 - Bias, Algorithms and Criminal Justice

Lots of algorithmic tools are now used to support decision-making in the criminal justice system. Many of them are criticised for being biased. What should be done about this? In this episode, I talk to Chelsea Barabas about this very question. Chelsea is a PhD candidate at MIT, where she examines the spread of algorithmic decision making tools in the US criminal legal system. She works with interdisciplinary researchers, government officials and community organizers to unpack and transform mainstream narratives around criminal justice reform and data-driven decision making. She is currently a Technology Fellow at the Carr Center for Human Rights Policy at the Harvard Kennedy School of Government. Formerly, she was a research scientist for the AI Ethics and Governance Initiative at the MIT Media Lab.

You can download the episode here or listen below. You can also subscribe on Apple Podcasts, Stitcher, Spotify and other podcasting services (the RSS feed is here).

Show notes

Topics covered in this show include

• The history of algorithmic decision-making in criminal justice

• Modern AI tools in criminal justice

• The problem of biased decision-making

• Examples of bias in practice

• The FAT (Fairness, Accountability and Transparency) approach to bias

• Can we de-bias algorithms using formal, technical rules?

• Can we de-bias algorithms through proper review and oversight?

• Should we be more critical of the data used to build these systems?

• Problems with pre-trial risk assessment measures

• The abolitionist perspective on criminal justice reform

Relevant Links

• Chelsea's homepage

• Chelsea on Twitter

• "Beyond Bias: Reimagining the terms "Ethical AI" in Criminal Law" by Chelsea

• Video presentation of this paper

• "Studying up: reorienting the study of algorithmic fairness around issues of power." by Chelsea and ors

• Kleinberg et al on the impossibility of fairness

• Kleinberg et al on using algorithms to detect discrimination

• The Condemnation of Blackness by Khalil Gibran Muhammad

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79 - Is There A Techno-Responsibility Gap?

 

What happens if an autonomous machine does something wrong? Who, if anyone, should be held responsible for the machine's actions? That's the topic I discuss in this episode with Daniel Tigard. Daniel Tigard is a Senior Research Associate in the Institute for History & Ethics of Medicine, at the Technical University of Munich. His current work addresses issues of moral responsibility in emerging technology. He is the author of several papers on moral distress and responsibility in medical ethics as well as, more recently, papers on moral responsibility and autonomous systems. 

You can download the episode here or listen below. You can also subscribe on Apple Podcasts, Stitcher, Spotify and other podcasting services (the RSS feed is here).

          

Show Notes

Topics discussed include:

 

• What is responsibility? Why is it so complex?

• The three faces of responsibility: attribution, accountability and answerability

• Why are people so worried about responsibility gaps for autonomous systems?

• What are some of the alleged solutions to the "gap" problem?

• Who are the techno-pessimists and who are the techno-optimists?

• Why does Daniel think that there is no techno-responsibility gap?

• Is our application of responsibility concepts to machines overly metaphorical?

 

Relevant Links

• Daniel's ResearchGATE profile

• Daniel's papers on Philpapers

• "There is no Techno-Responsibility Gap" by Daniel

• "Artificial Intelligence, Responsibility Attribution, and a Relational Justification of Explainability" by Mark Coeckelbergh

• Technologically blurred accountability? by Kohler, Roughley and Sauer

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