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Search-Based Fairness Testing: An Overview

arXiv 2311.06175 ↗First posted 10 Nov 2023cs.AIcited by 0

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Abstract (arXiv)

Artificial Intelligence (AI) has demonstrated remarkable capabilities in domains such as recruitment, finance, healthcare, and the judiciary. However, biases in AI systems raise ethical and societal concerns, emphasizing the need for effective fairness testing methods. This paper reviews current research on fairness testing, particularly its application through search-based testing. Our analysis highlights progress and identifies areas of improvement in addressing AI systems biases. Future research should focus on leveraging established search-based testing methodologies for fairness testing.

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References · 20

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8 more
  1. 2020Trust and Medical AI: The challenges we face and the expertise needed to overcome themcited by 21
  2. 2021Automatic Fairness Testing of Neural Classifiers through Adversarial Samplingcited by 14
  3. 2022TESTSGD: Interpretable Testing of Neural Networks Against Subtle Group Discriminationcited by 7
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  5. 2022Explainable Global Fairness Verification of Tree-Based Classifierscited by 5
  6. 2023FairRec: Fairness Testing for Deep Recommender Systemscited by 3
  7. 2022Enhanced Fairness Testing via Generating Effective Initial Individual Discriminatory Instancescited by 2
  8. 2022fAux: Testing Individual Fairness via Gradient Alignmentcited by 1

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