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The Multifaced Nature of Bias in AI: Impact on Model Generalization, Robustness, and Fairness

In: Maschinen wie wir?

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  • Eirini Ntoutsi

    (Universität der Bundeswehr München)

Abstract

Bias in Artificial Intelligence (AI) is a critical issue that has gained significant attention due to its association with discrimination and harm. Although bias has increasingly carried a negative connotation in recent years, it is not inherently positive or negative. In AI, bias can guide models toward desired outcomes and improve generalization, but it can also lead to discrimination against individuals or groups based on protected characteristics such as gender, race, or age, and undermine model robustness in varying contexts. This chapter explores the multifaceted nature of bias in AI, highlighting its benefits and drawbacks. We discuss how bias can be harnessed to improve models while addressing its negative effects, such as perpetuating inequalities and reducing robustness. The need to understand and manage bias is emphasized to ensure AI systems remain fair, ethical, and effective.

Suggested Citation

  • Eirini Ntoutsi, 2025. "The Multifaced Nature of Bias in AI: Impact on Model Generalization, Robustness, and Fairness," Springer Books, in: Burkhard Schäffer & Fabio Roman Lieder (ed.), Maschinen wie wir?, pages 231-247, Springer.
    • Handle: RePEc:spr:sprchp:978-3-658-48522-1_11
    DOI: 10.1007/978-3-658-48522-1_11
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