GeCo: Quality Counterfactual Explanations in Real Time

by   Maximilian Schleich, et al.

Machine learning is increasingly applied in high-stakes decision making that directly affect people's lives, and this leads to an increased demand for systems to explain their decisions. Explanations often take the form of counterfactuals, which consists of conveying to the end user what she/he needs to change in order to improve the outcome. Computing counterfactual explanations is challenging, because of the inherent tension between a rich semantics of the domain, and the need for real time response. In this paper we present GeCo, the first system that can compute plausible and feasible counterfactual explanations in real time. At its core, GeCo relies on a genetic algorithm, which is customized to favor searching counterfactual explanations with the smallest number of changes. To achieve real-time performance, we introduce two novel optimizations: Δ-representation of candidate counterfactuals, and partial evaluation of the classifier. We compare empirically GeCo against five other systems described in the literature, and show that it is the only system that can achieve both high quality explanations and real time answers.


page 10

page 12


Convex Density Constraints for Computing Plausible Counterfactual Explanations

The increasing deployment of machine learning as well as legal regulatio...

Convex optimization for actionable & plausible counterfactual explanations

Transparency is an essential requirement of machine learning based decis...

Directive Explanations for Actionable Explainability in Machine Learning Applications

This paper investigates the prospects of using directive explanations to...

A Series of Unfortunate Counterfactual Events: the Role of Time in Counterfactual Explanations

Counterfactual explanations are a prominent example of post-hoc interpre...

Counterfactual Explanations via Latent Space Projection and Interpolation

Counterfactual explanations represent the minimal change to a data sampl...

Decisions, Counterfactual Explanations and Strategic Behavior

Data-driven predictive models are increasingly used to inform decisions ...