FACE: Feasible and Actionable Counterfactual Explanations (1909.09369v2)

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., low-skilled 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.

• The paper proposes FACE, which generates counterfactual explanations that are both feasible and actionable, addressing limitations of traditional methods.
• It employs a density-weighted metric and shortest path approach to ensure counterfactuals reside in high-density regions while respecting real-world constraints.
• Empirical results on synthetic and real-world datasets demonstrate improved model interpretability and practicality, enhancing trust in decision-making systems.

Feasible and Actionable Counterfactual Explanations: An Expert Overview

The paper "FACE: Feasible and Actionable Counterfactual Explanations" addresses significant limitations in the domain of counterfactual explanations for decision-making processes facilitated by machine learning models. The authors Rafael Poyiadzi et al. propose a novel approach to generating counterfactual explanations that emphasizes not only the feasibility of the prescribed changes but also their actionability, thereby making these explanations more practical for real-world application.

Critical Analysis of Conventional Counterfactual Explanation Approaches

Traditional methods for generating counterfactual explanations often rely on the notion of the "closest possible world," whereby minimal changes to the input features result in a desired model prediction. However, the authors argue that such approaches suffer from two crucial flaws. First, they may generate counterfactuals that do not align with the underlying data distribution, leading to unrealistic or unachievable goals. For example, advising an applicant with limited skills to significantly increase their income without considering the feasibility of improving their skill set presents an implausible scenario. Second, conventional methods may fail to provide a clear path for achieving the suggested changes, resulting in actionable recourses that are nonviable for the individual concerned.

Introduction of FACE Algorithm

The introduction of the FACE algorithm represents a shift towards counterfactual explanations that are not only feasible by being representative of the underlying data distribution but also actionable via identifiable paths. FACE operates by defining feasible paths using shortest path distances within a framework of density-weighted metrics. This ensures the generated counterfactuals lie in high-density regions and are connected via feasible and high-density paths from the original state. The algorithm respects constraints imposed by real-world conditions by considering contextual features and known constraints, thus prioritizing feasible transformations.

Methodological Contributions and Implications

The authors present a comprehensive methodology for deploying FACE, providing empirical results using both synthetic and real-world data sets, such as the MNIST data for digit classification. The experimental results demonstrate the effectiveness of FACE in producing counterfactual paths that are coherent and actionable, addressing the limitations of prior models.

From a theoretical perspective, FACE aligns counterfactual explanations more closely with human-like reasoning by accounting for both feasibility and actionability in the decision-making process. Practically, this methodology can greatly enhance model interpretability and trust, particularly in sensitive domains such as finance and healthcare.

Future Directions and Developments

While FACE demonstrates a significant advancement in generating meaningful counterfactual explanations, there remains room for further exploration and refinement. Future research could focus on extending the FACE approach to accommodate dynamic data sets and complex models beyond the current scope. Additionally, evaluating the real-world impact of FACE on decision-making processes for various stakeholders can provide deeper insights into improving model interpretability.

In conclusion, this paper contributes valuable insights and methods for improving the practical utility of counterfactual explanations in machine learning. By addressing feasibility and actionability, FACE offers a pragmatic solution to enhance the trust and effectiveness of model-supported decision-making processes.