Decisions, Counterfactual Explanations and Strategic Behavior

by   Stratis Tsirtsis, et al.

Data-driven predictive models are increasingly used to inform decisions that hold important consequences for individuals and society. As a result, decision makers are often obliged, even legally required, to provide explanations about their decisions. In this context, it has been increasingly argued that these explanations should help individuals understand what would have to change for these decisions to be beneficial ones. However, there has been little discussion on the possibility that individuals may use the above counterfactual explanations to invest effort strategically in order to maximize their chances of receiving a beneficial decision. In this paper, our goal is to find policies and counterfactual explanations that are optimal in terms of utility in such a strategic setting. To this end, we first show that, given a pre-defined policy, the problem of finding the optimal set of counterfactual explanations is NP-hard. However, we further show that the corresponding objective is nondecreasing and satisfies submodularity. Therefore, a standard greedy algorithm offers an approximation factor of (1-1/e) at solving the problem. Additionally, we also show that the problem of jointly finding both the optimal policy and set of counterfactual explanations reduces to maximizing a non-monotone submodular function. As a result, we can use a recent randomized algorithm to solve the problem, which offers an approximation factor of 1/e. Finally, we illustrate our theoretical findings by performing experiments on synthetic and real lending data.


page 1

page 2

page 3

page 4


Optimal Decision Making Under Strategic Behavior

We are witnessing an increasing use of data-driven predictive models to ...

Disagreement amongst counterfactual explanations: How transparency can be deceptive

Counterfactual explanations are increasingly used as an Explainable Arti...

Actionable Interpretability through Optimizable Counterfactual Explanations for Tree Ensembles

Counterfactual explanations help users understand why machine learned mo...

Model-Agnostic Counterfactual Explanations for Consequential Decisions

Predictive models are being increasingly used to support consequential d...

ViCE: Visual Counterfactual Explanations for Machine Learning Models

The continued improvements in the predictive accuracy of machine learnin...

Rethinking Counterfactual Explanations as Local and Regional Counterfactual Policies

Among the challenges not yet resolved for Counterfactual Explanations (C...

Scaling Guarantees for Nearest Counterfactual Explanations

Counterfactual explanations (CFE) are being widely used to explain algor...

Please sign up or login with your details

Forgot password? Click here to reset