Evaluating Decision Makers over Selectively Labelled Data: A Causal Modelling Approach

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Sammanfattning

We present a Bayesian approach to evaluate AI decision systems using data from past decisions. Our approach addresses two challenges that are typically encountered in such settings and prevent a direct evaluation. First, the data may not have included all factors that affected past decisions. And second, past decisions may have led to unobserved outcomes. This is the case, for example, when a bank decides whether a customer should be granted a loan, and the outcome of interest is whether the customer will repay the loan. In this case, the data includes the outcome (if loan was repaid or not) only for customers who were granted the loan, but not for those who were not. To address these challenges, we formalize the decision making process with a causal model, considering also unobserved features. Based on this model, we compute counterfactuals to impute missing outcomes, which in turn allows us to produce accurate evaluations. As we demonstrate over real and synthetic data, our approach estimates the quality of decisions more accurately and robustly compared to previous methods.
Originalspråkengelska
Titel på värdpublikationDiscovery Science : DS 2020
Antal sidor16
UtgivningsortCham
FörlagSpringer
Utgivningsdatum2020
Sidor3-18
ISBN (tryckt)978-3-030-61526-0
ISBN (elektroniskt)978-3-030-61527-7
DOI
StatusPublicerad - 2020
MoE-publikationstypA4 Artikel i en konferenspublikation
EvenemangInternational Conference in Discovery Science - Thessaloniki, Grekland
Varaktighet: 19 okt. 202021 okt. 2020
Konferensnummer: 23
https://ds2020.csd.auth.gr/

Publikationsserier

NamnLecture Notes in Computer Science
FörlagSpringer
Volym12323
ISSN (tryckt)0302-9743
ISSN (elektroniskt)1611-3349

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