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.