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CERTIFAI: A Common Framework to Provide Explanations and Analyse the Fairness and Robustness of Black-box Models
Open accessAbstract
Concerns within the machine learning community and external pressures from regulators over the vulnerabilities of machine learning algorithms have spurred on the fields of explainability, robustness, and fairness. Often, issues in explainability, robustness, and fairness are confined to their specific sub-fields and few tools exist for model developers to use to simultaneously build their modeling pipelines in a transparent, accountable, and fair way. This can lead to a bottleneck on the model developer's side as they must juggle multiple methods to evaluate their algorithms. In this paper, we present a single framework for analyzing the robustness, fairness, and explainability of a classifier. The framework, which is based on the generation of counterfactual explanations through a custom genetic algorithm, is flexible, model-agnostic, and does not require access to model internals. The framework allows the user to calculate robustness and fairness scores for individual models and generate explanations for individual predictions which provide a means for actionable recourse (changes to an input to help get a desired outcome). This is the first time that a unified tool has been developed to address three key issues pertaining towards building a responsible artificial intelligence system.
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“In addition, counterfactual reasoning is claimed to be a key mechanism for explaining adaptive behavior in a changing environment [25], [26].”
From A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021) · cites this paper as [25]
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Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423
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Explainable machine learning in deployment
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Self-citationCounterfactual explanations and how to find them: literature review and benchmarking
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“…[159], gradient boosted decision trees [147], support vector machines [131], [138], [146], random forests [81], [86], [142]–[144], neural networks [6], [48], [49], [91], [129], [130], [133], [135], [139], [141], [145], [148], [151], or combinations of these [100], [105], [134], [152], [154], [160].”
Metadata
- Authors
- Sharma, S., Henderson, J., & Ghosh, J.
- Affiliations
- The University of Texas at Austin (US)
- CognitiveScale, Austin, TX, USA
- Venue
- Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society
- Publisher
- ACM
- Published
- 2020-02-07
- Pages
- 166-172
- ISSN
- 3065-8365 (ISSN-L)
- OpenAlex
- W2945151003
- Cited by
- 226
- References
- 9
- Citations by year
- 2026: 15 · 2025: 27 · 2024: 46 · 2023: 42 · 2022: 35 · 2021: 43 · 2020: 14
- FWCI
- 12.78
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Proceedings of the AAAI/ACM Conference on AI Ethics and Society (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Keywords
- explainable AI, robustness, algorithmic fairness, counterfactual explanations, genetic algorithm, classifier evaluation, model-agnostic evaluation, recourse, responsible AI
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