Algorithmic Credit Scoring in Consumer Lending

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).

Show Notes

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

Relevant Links

• 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

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