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Algorithmic Factors Influencing Bias in Machine Learning

Part of the Communications in Computer and Information Science book series (CCIS,volume 1524)


It is fair to say that many of the prominent examples of bias in Machine Learning (ML) arise from bias in the training data. In fact, some would argue that supervised ML algorithms cannot be biased, they reflect the data on which they are trained. In this paper, we demonstrate how ML algorithms can misrepresent the training data through underestimation. We show how irreducible error, regularization, and feature and class imbalance can contribute to this underestimation. The paper concludes with a demonstration of how the careful management of synthetic counterfactuals can ameliorate the impact of this underestimation bias.


  • Bias
  • Fairness
  • Model capacity
  • Regularisation

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This work was funded by Science Foundation Ireland through the SFI Centre for Research Training in Machine Learning (Grant No. 18/CRT/6183) with support from Microsoft Ireland.

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Correspondence to William Blanzeisky .

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Blanzeisky, W., Cunningham, P. (2021). Algorithmic Factors Influencing Bias in Machine Learning. In: Kamp, M., et al. Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2021. Communications in Computer and Information Science, vol 1524. Springer, Cham.

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-93735-5

  • Online ISBN: 978-3-030-93736-2

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