Inclusive AI: Mitigating Algorithmic Discrimination for a Fairer Society

Algorithmic fairness is increasingly critical as artificial intelligence (AI) and machine learning (ML) systems shape decisions in hiring, healthcare, finance, law enforcement, and social media. Bias in digital systems perpetuates systemic disadvantages for marginalised groups, rooted in historical inequalities embedded in data, model design, and deployment. This chapter explores the foundations of bias through ethical lenses such as utilitarianism, deontology, and virtue ethics, examining trade-offs in fairness definitions. Evidence from hiring algorithms, predictive policing, credit scoring, and healthcare diagnostics illustrates the societal risks of unchecked algorithmic bias. While technical measures like fairness constraints and audits provide partial solutions, they must align with regulatory standards and ethical governance. Addressing bias requires an interdisciplinary approach that integrates AI ethics, policy, and industry accountability to ensure fairness and transparency in AI-driven decisions.