Speaker: Eden Saig

Title: Delegated Classification

Abstract: What happens when machine learning is outsourced to profit-maximizing agents?

In this work, we propose a theoretical framework for incentive-aware delegation of machine learning tasks. We model delegation as a principal-agent game, in which accurate learning can be incentivized by the principal using performance-based...

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Speaker: Yonatan Sompolinsky (Harvard)

Abstract: We tackle the problem of Miners Extracted Value (MEV), where a transaction sequencer extracts profit off users, by frontrunning pending transactions, sandwiching others, or carrying out any other form of ordering manipulations. Most transaction settlement services are designed such that, in each round, a single sequencer is granted full, unrestricted permission to sequence pending transactions. Accordingly, to undermine sequencer monopoly, we devise an auction that forces competition...

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Speaker: Berk Ustun (UCSD)

Title: When Personalization Harms Performance
 

Abstract: Clinical prediction models often encode group attributes like sex, age, and HIV status for personalization – i.e., to assign more accurate predictions to heterogeneous subpopulations. In this talk, I will describe how such practices inadvertently lead to worsenalization, by assigning unnecessarily inaccurate predictions to minority groups. I will discuss how these effects violate our basic expectations from personalization...

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Speaker: John Horton (MIT)

Title: Automated Social Science: A Structural Causal Model-Based Approach

Abstract:

We present an approach for automatically generating and testing, in silico, social scientific...

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