Face

Abstract

Work in Counterfactual Explanations tends to focus on the principle of "the closest possible world" that identifies small changes leading to the desired outcome. In this paper we argue that while this approach might initially seem intuitively appealing it exhibits shortcomings not addressed in the current literature. First, a counterfactual example generated by the state-of-the-art systems is not necessarily representative of the underlying data distribution, and may therefore prescribe unachievable goals (e.g., an unsuccessful life insurance applicant with severe disability may be advised to do more sports). Secondly, the counterfactuals may not be based on a "feasible path" between the current state of the subject and the suggested one, making actionable recourse infeasible (e.g., lowskilled unsuccessful mortgage applicants may be told to double their salary, which may be hard without first increasing their skill level). These two shortcomings may render counterfactual explanations impractical and sometimes outright offensive. To address these two major flaws, first of all, we propose a new line of Counterfactual Explanations research aimed at providing actionable and feasible paths to transform a selected instance into one that meets a certain goal. Secondly, we propose FACE: an algorithmically sound way of uncovering these "feasible paths" based on the shortest path distances defined via density-weighted metrics. Our approach generates counterfactuals that are coherent with the underlying data distribution and supported by the "feasible paths" of change, which are achievable and can be tailored to the problem at hand. CCS CONCEPTS • Computing methodologies → Machine learning.

Explainable machine learning for public policy: Use cases, gaps, and research directions

“…The focus of algorithmic recourse work has been on using counterfactual explanations. As simple counterfactual explanations do not guarantee explanations with actionable changes, there has been a range of approaches proposed for deriving counterfactual explanations that are diverse, sparse, plausible, and actionable Karimi et al, 2020Karimi et al, , 2021bMothilal et al, 2020;Poyiadzi et al, 2020;Upadhyay et al, 2021). Karimi and colleagues provide a survey of methods for algorithmic recourse including in Karimi et al (2021a).…”

“…gBaehrens et al (2010),Simonyan et al (2013),Zeiler and Fergus, (2014), Bach et al (2015). hWachter et al (2018),,Mothilal et al (2020),Poyiadzi et al (2020). iKim et al (2016),Koh and Liang, (2017).…”

SCGAN: Sparse CounterGAN for Counterfactual Explanations in Breast Cancer Prediction

“…This approach leads to more interpretable and comprehensible explanations for users. Feasibility addresses the concern that identifying the nearest counterfactual to an instance may not result in a feasible modification of the features [32]. It stipulates that a generated counterfactual explanation should be practically achievable in the real world.…”

Explanation of Siamese Neural Networks for Weakly Supervised Learning

“…As shown by Moore et al [28], this approach strongly depends on the size and quality of the considered training set, and it cannot find a counterfactual that is not explicitly in the set. Therefore, a lot of new methods of the counterfactual explanation have been developed [29,30,31,32,33,34,35].…”