OUR SPEAKERS

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I'm a second year PhD fellow at the Department of Informatics, University of Oslo. My research focuses on algorithmic fairness in dynamic processes, but my interests also include uncertainty, explainability, set selection and matching problems. Prior to starting my PhD, I interned at Google Brain Paris, where I worked on ML fairness using optimal transport.
I have completed my MSc in Computer Science and BSc in Computer Science and Computational Biology, both at the Hebrew University of Jerusalem, during which I was working on link prediction in heterogeneous graphs. Alongside my studies, I worked as a data scientist (student position) at Intel Advanced Analytics and as a teaching assistant.

Meirav Segal

PhD Fellow
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English, Hebrew
Languages:
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Location:
Oslo, Norway
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Can also give an online talk/webinar
Paid only. Contact speaker for pricing!

MY TALKS

Policy Fairness in Sequential Allocations under Bias Dynamics

Data / AI / ML

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This work considers a dynamic decision making framework for allocating opportunities over time to advantaged and disadvantaged individuals. Here, individuals in the disadvantaged group are assumed to experience a societal bias that limits their success probability. A policy of allocating opportunities stipulates thresholds on the success probability for the advantaged and disadvantaged group.
We analyse the interplay between utility and a novel measure of fairness for different dynamics that dictate how the societal bias changes based on the current thresholds while the group sizes are fixed. Our analysis is supported by experimental results on synthetic data for the use case of college admissions.

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Policy Fairness in Sequential Allocations under Bias Dynamics

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