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Bias, Fairness and Accountability with Artificial Intelligence and Machine Learning Algorithms

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Listed:
  • Nengfeng Zhou
  • Zach Zhang
  • Vijayan N. Nair
  • Harsh Singhal
  • Jie Chen

Abstract

The advent of artificial intelligence (AI) and machine learning algorithms has led to opportunities as well as challenges in their use. In this overview paper, we begin with a discussion of bias and fairness issues that arise with the use of AI techniques, with a focus on supervised machine learning algorithms. We then describe the types and sources of data bias and discuss the nature of algorithmic unfairness. In addition, we provide a review of fairness metrics in the literature, discuss their limitations, and describe de‐biasing (or mitigation) techniques in the model life cycle.

Suggested Citation

  • Nengfeng Zhou & Zach Zhang & Vijayan N. Nair & Harsh Singhal & Jie Chen, 2022. "Bias, Fairness and Accountability with Artificial Intelligence and Machine Learning Algorithms," International Statistical Review, International Statistical Institute, vol. 90(3), pages 468-480, December.
    • Handle: RePEc:bla:istatr:v:90:y:2022:i:3:p:468-480
    DOI: 10.1111/insr.12492
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    References listed on IDEAS

    as
    1. Linwei Hu & Jie Chen & Joel Vaughan & Soroush Aramideh & Hanyu Yang & Kelly Wang & Agus Sudjianto & Vijayan N. Nair, 2021. "Supervised Machine Learning Techniques: An Overview with Applications to Banking," International Statistical Review, International Statistical Institute, vol. 89(3), pages 573-604, December.
    2. Tobias Berg & Valentin Burg & Ana Gombović & Manju Puri, 2020. "On the Rise of FinTechs: Credit Scoring Using Digital Footprints," The Review of Financial Studies, Society for Financial Studies, vol. 33(7), pages 2845-2897.
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