Talking law and economics at ETH Zurich podcast

Prediction without Preclusion – Berk Ustun (UC San Diego)

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14:59
15 Sekunden vorwärts
15 Sekunden vorwärts

In this episode of the CLE Vlog & Podcast Series, Prof. Berk Ustun (University of California San Diego) discusses his paper "Prediction without Preclusion: Recourse Verification with Reachable Sets" with Benjamin Kohler (ETH Zurich).

In their work, Berk Ustun and his co-authors investigate how machine learning models in high-stakes settings, such as lending and hiring, assign "fixed" predictions that individuals cannot change regardless of their actions. They introduce a formal procedure called "recourse verification" to certify whether a model allows for responsiveness or precludes access. In addition, they develop an auditing tool for practitioners to flag models that effectively block access to certain outcomes before they are deployed – a crucial step to promote fairness and transparency in machine-led decision making.


Paper Reference:

Berk Ustun – University of Southern California, San Diego

Avni Kothari – University of Southern California, San Diego

Bogdan Kulynych – Lausanne University Hospital

Tsui-Wei Weng – Halıcıoğlu Data Science Institute


Prediction without Preclusion: Recourse Verification with Reachable Sets

https://arxiv.org/abs/2308.12820


Audio Credits for Trailer:

AllttA by AllttA

https://youtu.be/ZawLOcbQZ2w

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