Avoidable and Unavoidable AI Algorithmic Bias

This chapter explores the ethical implications of artificial intelligence (AI) algorithms, focusing on avoidable and unavoidable discrimination. Avoidable discrimination can be addressed through improved data governance, algorithm design, and regulatory practices, while unavoidable discrimination is inherent limitations due to technological constraints or legal and ethical standards. The study uses case studies from healthcare, finance, and interstate conflict to illustrate the impact of both types of discrimination. A multi-disciplinary approach proposes a framework for identifying, assessing, and addressing both forms of discrimination. Strategies for avoidable discrimination include data augmentation, algorithmic transparency, and fairness-aware machine learning techniques. The chapter recommends best practices for AI developers, policymakers, and regulatory bodies to create efficient, innovative, and fair AI systems that minimize algorithmic discrimination.