Human rights instruments in jurisdictions throughout the world assert the right to equality and non-discrimination. These principles lie at the heart of human rights. This chapter initially considers the meanings of equality and non-discrimination. It then examines equality and non-discrimination in the context of Article 14 of the ECHR. This includes an examination of direct and indirect discrimination and intersectionality. Examples from the Strasbourg jurisprudence illustrate the applicability of Article 14 ECHR to specific protected categories including race/ethnicity, religion, sex, gender, sexual orientation, gender identity, and sex characteristics. Article 14 ECHR and its influence in domestic UK law is analysed. The final section considers the UK Equality Act 2010 (EqA)and the complexities raised by competing equality claims. There are case studies concerning sexual orientation and religious belief, employment law, education and a discussion of the concept of ‘woman’ and ‘sex’ in the EqA as referring to a biological woman and biological sex.

This chapter examines the principle of equality and the right to nondiscrimination in international human rights law. It covers the normative foundations, legal classification of discriminatory treatment, and the international legal regime of the right to nondiscrimination. The chapter explores how international human rights instruments define and address discrimination, the grounds for discrimination, and the obligations of states to eliminate discriminatory practices. It also discusses the mechanisms for monitoring and enforcing nondiscrimination, including the role of international bodies and national institutions. The chapter highlights the challenges in achieving substantive equality and the importance of adopting comprehensive measures to address both direct and indirect discrimination.

This chapter analyzes challenges to AI decision-making based on anti-discrimination in the US, the UK, and Australia. Machine learning algorithms can be trained on datasets that contain human bias, thus resulting in predictions that are tainted with unfair discrimination. Anti-discrimination claims involve challenging the inputs of decision-making, such as the data or source code, and arguing that the flawed algorithm or data inputted into the AI system leads to discriminatory outcomes.