Projects per year
Abstract
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.
Original language | English |
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Title of host publication | Discovery Science : DS 2020 |
Number of pages | 16 |
Place of Publication | Cham |
Publisher | Springer |
Publication date | 2020 |
Pages | 3-18 |
ISBN (Print) | 978-3-030-61526-0 |
ISBN (Electronic) | 978-3-030-61527-7 |
DOIs | |
Publication status | Published - 2020 |
MoE publication type | A4 Article in conference proceedings |
Event | International Conference in Discovery Science - Thessaloniki, Greece Duration: 19 Oct 2020 → 21 Oct 2020 Conference number: 23 https://ds2020.csd.auth.gr/ |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer |
Volume | 12323 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Fields of Science
- 113 Computer and information sciences
Projects
- 1 Active
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Machine Learning Management Systems
Mahadevan, A. & Mathioudakis, M.
01/01/2020 → 31/12/2023
Project: University core funding