Personalized Algorithmic Recourse with Preference Elicitation
- URL: http://arxiv.org/abs/2205.13743v5
- Date: Tue, 23 Jan 2024 17:53:23 GMT
- Title: Personalized Algorithmic Recourse with Preference Elicitation
- Authors: Giovanni De Toni, Paolo Viappiani, Stefano Teso, Bruno Lepri, Andrea
Passerini
- Abstract summary: We introduce PEAR, the first human-in-the-loop approach capable of providing personalized algorithmic recourse tailored to the needs of any end-user.
PEAR builds on insights from Bayesian Preference Elicitation to iteratively refine an estimate of the costs of actions by asking choice set queries to the target user.
Our empirical evaluation on real-world datasets highlights how PEAR produces high-quality personalized recourse in only a handful of iterations.
- Score: 20.78332455864586
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Algorithmic Recourse (AR) is the problem of computing a sequence of actions
that -- once performed by a user -- overturns an undesirable machine decision.
It is paramount that the sequence of actions does not require too much effort
for users to implement. Yet, most approaches to AR assume that actions cost the
same for all users, and thus may recommend unfairly expensive recourse plans to
certain users. Prompted by this observation, we introduce PEAR, the first
human-in-the-loop approach capable of providing personalized algorithmic
recourse tailored to the needs of any end-user. PEAR builds on insights from
Bayesian Preference Elicitation to iteratively refine an estimate of the costs
of actions by asking choice set queries to the target user. The queries
themselves are computed by maximizing the Expected Utility of Selection, a
principled measure of information gain accounting for uncertainty on both the
cost estimate and the user's responses. PEAR integrates elicitation into a
Reinforcement Learning agent coupled with Monte Carlo Tree Search to quickly
identify promising recourse plans. Our empirical evaluation on real-world
datasets highlights how PEAR produces high-quality personalized recourse in
only a handful of iterations.
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