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CERTIFAI: A Common Framework to Provide Explanations and Analyse the Fairness and Robustness of Black-box Models
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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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Rhea, A. K., Markey, K., D’Arinzo, L., Schellmann, H., Sloane, M., Squires, P., & Stoyanovich, J. (2022) · AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3514094.3534189 · Cited by 19
Integration of Generative AI and Mobile Networking: A Comprehensive Survey
Li, J., He, M., Zhou, C., Huang, X., Liu, Z., Zhao, L. Y., Wang, C., & Wu, H. (2025) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2025.3639401 · Cited by 19
Evolutionary Approaches to Explainable Machine Learning
Zhou, R., & Hu, T. (2023) · Genetic and evolutionary computation · doi:10.1007/978-981-99-3814-8_16 · Cited by 16
Manifestations of xenophobia in AI systems
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FairXAI - A Taxonomy and Framework for Fairness and Explainability Synergy in Machine Learning
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FIMAP: Feature Importance by Minimal Adversarial Perturbation
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FACET: Robust Counterfactual Explanation Analytics
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GeCo: Quality Counterfactual Explanations in Real TimePreprint
Schleich, M., Geng, Z., Zhang, Y., & Suciu, D. M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.01292 · Cited by 13
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Measuring the Burden of (Un)fairness Using Counterfactuals
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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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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