Are you being watched, tracked and traced every minute of the day? Probably. The digital world thrives on surveillance. What should we do about this? My guest today is Carissa Véliz. Carissa is an Associate Professor at the Faculty of Philosophy and the Institute of Ethics in AI at Oxford University. She is also a Tutorial Fellow at Hertford College Oxford. She works on privacy, technology, moral and political philosophy and public policy. She has also been a guest on this podcast on two previous occasions. Today, we’ll be talking about her recently published book Privacy is Power.

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). 

Topics discussed in this show include:

• The most surprising examples of digital surveillance
• The nature of privacy
• Is privacy dead?
• Privacy as an intrinsic and instrumental value
• The relationship between privacy and autonomy
• Does surveillance help with security and health?
• The problem with mass surveillance
• The phenomenon of toxic data
• How surveillance undermines democracy and freedom
• Are we willing to trade privacy for convenient services?
• And much more

• Carissa's Webpage
• Privacy is Power by Carissa
• Summary of Privacy is Power in Aeon
• Review of Privacy is Power in The Guardian
• Carissa's Twitter feed (a treasure trove of links about privacy and surveillance)
• Views on Privacy: A Survey by Sian Brooke and Carissa Véliz
• Data, Privacy and the Individual by Carissa Véliz

In today's episode, I talk to Nikita Aggarwal about the legal and regulatory aspects of AI and algorithmic governance. We focus, in particular, on three topics: (i) algorithmic credit scoring; (ii) the problem of 'too big to fail' tech platforms and (iii) AI crime. Nikita is a DPhil (PhD) candidate at the Faculty of Law at Oxford, as well as a Research Associate at the Oxford Internet Institute's Digital Ethics Lab. Her research examines the legal and ethical challenges due to emerging, data-driven technologies, with a particular focus on machine learning in consumer lending. Prior to entering academia, she was an attorney in the legal department of the International Monetary Fund, where she advised on financial sector law reform in the Euro area.

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

Topics discussed include:

• The digitisation, datafication and disintermediation of consumer credit markets

• Algorithmic credit scoring

• The problems of risk and bias in credit scoring

• How law and regulation can address these problems

• Tech platforms that are too big to fail

• What should we do if Facebook fails?

• The forms of AI crime

• How to address the problem of AI crime

• Nikita's homepage

• Nikita on Twitter

• 'The Norms of Algorithmic Credit Scoring' by Nikita

• 'What if Facebook Goes Down? Ethical and Legal Considerations for the Demise of Big Tech Platforms' by Carl Ohman and Nikita

• 'Artificial Intelligence Crime: An Interdisciplinary Analysis of Foreseeable Threats and Solutions' by Thomas King, Nikita, Mariarosario Taddeo and Luciano Floridi

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).

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

• 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