mlsanityData to 7 Oct 2026

Bias, Fairness, and Accountability with AI and ML Algorithms

arXiv 2105.06558 ↗First posted 13 May 2021stat.ML cs.LGcited by 3

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Abstract (arXiv)

The advent of AI and ML algorithms has led to opportunities as well as challenges. In this paper, we provide an overview of bias and fairness issues that arise with the use of ML algorithms. We describe the types and sources of data bias, and discuss the nature of algorithmic unfairness. This is followed by a review of fairness metrics in the literature, discussion of their limitations, and a description of de-biasing (or mitigation) techniques in the model life cycle.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 26 of its 69 references that are in the map. First posted on arXiv 13 May 2021.

Builds on

26 of 69 references.

References · 26

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14 more
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Cited by year

Citing papers per year: 2023: 2; 2024: 1.

3 papers.

  1. 2024Properties of fairness measures in the context of varying class imbalance and protected group ratioscited by 7
  2. 2023Fair-CDA: Continuous and Directional Augmentation for Group Fairnesscited by 3
  3. 2023Wearable-based Fair and Accurate Pain Assessment Using Multi-Attribute Fairness Loss in Convolutional Neural Networkscited by 1