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Title: The skyline of counterfactual explanations for machine learning decision models
Authors: Wang, Yongjie
Ding, Qinxu
Wang, Ke
Liu, Yue
Wu, Xingyu
Wang, Jinglong
Liu, Yong
Miao, Chunyan
Keywords: Engineering::Computer science and engineering
Issue Date: 2021
Source: Wang, Y., Ding, Q., Wang, K., Liu, Y., Wu, X., Wang, J., Liu, Y. & Miao, C. (2021). The skyline of counterfactual explanations for machine learning decision models. 30th ACM International Conference on Information & Knowledge Management (CIKM '21), 2030-2039.
Project: AISG-GC2019-003 
metadata.dc.contributor.conference: 30th ACM International Conference on Information & Knowledge Management (CIKM '21)
Abstract: Counterfactual explanations are minimum changes of a given input to alter the original prediction by a machine learning model, usually from an undesirable prediction to a desirable one. Previous works frame this problem as a constrained cost minimization, where the cost is defined as L1/L2 distance (or variants) over multiple features to measure the change. In real-life applications, features of different types are hardly comparable and it is difficult to measure the changes of heterogeneous features by a single cost function. Moreover, existing approaches do not support interactive exploration of counterfactual explanations. To address above issues, we propose the skyline counterfactual explanations that define the skyline of counterfactual explanations as all non-dominated changes. We solve this problem as multi-objective optimization over actionable features. This approach does not require any cost function over heterogeneous features. With the skyline, the user can interactively and incrementally refine their goals on the features and magnitudes to be changed, especially when lacking prior knowledge to express their needs precisely. Intensive experiment results on three real-life datasets demonstrate that the skyline method provides a friendly way for finding interesting counterfactual explanations, and achieves superior results compared to the state-of-the-art methods.
ISBN: 9781450384469
DOI: 10.1145/3459637.3482397
Schools: School of Computer Science and Engineering 
Research Centres: Alibaba-NTU Singapore Joint Research Institute
Rights: © 2021 Association for Computing Machinery. All rights reserved. This paper was published in the Proceedings of 30th ACM International Conference on Information & Knowledge Management (CIKM '21) and is made available with permission of Association for Computing Machinery.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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