Iterative Partial Fulfillment of Counterfactual Explanations: Benefits and Risks
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3600211.3604656.pdf
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1005.55 KB
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8bbb2975c9a077d3a9b360d11c9e4ba0
Author(s)
Zhou, Yilun
Date Issued
August 29, 2023
Publisher
Association for Computing Machinery
Citation
Yilun Zhou. 2023. Iterative Partial Fulfillment of Counterfactual Explanations: Benefits and Risks. In Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society (AIES '23). Association for Computing Machinery, New York, NY, USA, 248–258.
Version
Final published version
Abstract
Counterfactual (CF) explanations, also known as contrastive explanations and algorithmic recourses, are popular for explaining machine learning models in high-stakes domains. For a subject that receives a negative model prediction (e.g., mortgage application denial), the CF explanations are similar instances but with positive predictions, which informs the subject of ways to improve. While their various properties have been studied, such as validity and stability, we contribute a novel one: their behaviors under iterative partial fulfillment (IPF). Specifically, upon receiving a CF explanation, the subject may only partially fulfill it before requesting a new prediction with a new explanation, and repeat until the prediction is positive. Such partial fulfillment could be due to the subject’s limited capability (e.g., can only pay down two out of four credit card accounts at this moment) or an attempt to take the chance (e.g., betting that a monthly salary increase of $800 is enough even though $1,000 is recommended). Does such iterative partial fulfillment increase or decrease the total cost of improvement incurred by the subject? We mathematically formalize IPF and demonstrate, both theoretically and empirically, that different CF algorithms exhibit vastly different behaviors under IPF. We discuss implications of our observations, advocate for this factor to be carefully considered in the development and study of CF algorithms, and give several directions for future work.
Description
AIES ’23, August 08–10, 2023, Montréal, QC, Canada
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1145/3600211.3604656
