AI Safety and Fairness

Dilli Prasad Sharma (), Arash Habibi Lashkari (), Mahdi Daghmehchi Firoozjaei (), Samaneh Mahdavifar () and Pulei Xiong ()
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Dilli Prasad Sharma: University of Toronto
Arash Habibi Lashkari: York University
Mahdi Daghmehchi Firoozjaei: MacEwan University
Samaneh Mahdavifar: McGill University
Pulei Xiong: National Research Council of Canada

Chapter Chapter 11 in Understanding AI in Cybersecurity and Secure AI, 2025, pp 221-236 from Springer

Abstract: Abstract This chapter explores the critical issues of AI safety and fairness, focusing on the risks, ethical considerations, and challenges of developing responsible AI systems. It begins with analyzing potential AI risks, emphasizing the need for transparency, accountability, and trustworthiness. The chapter then delves into AI alignment with human values and machine ethics, introducing the four key principles (RICE) that serve as a foundation for ethical AI development. Additionally, it examines bias and fairness in AI, discussing the sources of bias, their impact on decision-making, and strategies to mitigate unfair outcomes.

Date: 2025
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DOI: 10.1007/978-3-031-91524-6_11

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