CERTIFAI: Counterfactual Explanations for Robustness, Transparency, Interpretability, and Fairness of Artificial Intelligence models

By Shubham Sharma, Jette Henderson, Joydeep Ghosh. 2019

102 citations recorded by the source corpus.

As artificial intelligence plays an increasingly important role in our society, there are ethical and moral obligations for both businesses and researchers to ensure that their machine learning models are designed, deployed, and maintained responsibly. These models need to be rigorously audited for fairness, robustness, transparency, and interpretability. A variety of methods have been developed that focus on these issues in isolation, however, managing these methods in conjunction with model development can be cumbersome and timeconsuming. In this paper, we introduce a unified and model-agnostic approach to address these issues: Counterfactual Explanations for Robustness, Transparency, Interpretability, and Fairness of Artificial Intelligence models (CERTIFAI). Unlike previous methods in this domain, CERTIFAI is a general tool that can be applied to any black-box model and any type of input data. Given a model and an input instance, CERTIFAI uses a custom genetic algorithm to generate counterfactuals: instances close to the input that change the prediction of the model. We demonstrate how these counterfactuals can be used to examine issues of robustness, interpretability, transparency, and fairness. Additionally, we introduce CERScore, the first black-box model robustness score that performs comparably to methods that have access to model internals.

Subfields: Safety, Robustness & Interpretability, AI Ethics, Fairness & Society

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Citations in the corpus

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, positive): “Finally, CERTI-FAI (Counterfactual Explanations for Robustness, Transparency, Inter-pretability, and Fairness of Artiﬁcial Intelligence models) [402] uses a customized genetic algorithm to generate counterfactuals that can help to see the robustness of a ML model, generate explanations, and examine…”
Data-centric Artificial Intelligence: A Survey (2023, unknown): “White-box methods necessitate access to the evaluated models, which can be achieved through complete internals [36, 111, 138], gradients [225], or solely the prediction function [52, 57, 127, 202].”
Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review (2020, unknown): “datapoint; therefore, they require to be run once per input to get several counterfactuals [48, 67, 97, 126, 167, 210, 224, 267, 278].”
Multi-Objective Counterfactual Explanations (2020, methodology): “Several model-agnostic counterfactual methods have been proposed [16,9,6,14,23,27,33].”