Prediction without Preclusion – Berk Ustun (UC San Diego)

About this episode

Prof. Berk Ustun discusses his paper on machine learning models and their implications for fairness and access in high-stakes settings.

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 California, San Diego Avni Kothari – University of 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…

People in this episode

Guest: Berk Ustun

Topics covered

• law
• economics
• machine learning
• fairness
• transparency

Keywords

• recourse verification
• auditing tool
• high-stakes settings
• lending
• hiring

Mentioned in this episode

Products: recourse verification, auditing tool

Books & works: Prediction without Preclusion, Prediction without Preclusion: Recourse Verification with Reachable Sets

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