AI Algorithmic Bias, Neurodiscrimination, and Neurorights: Towards Conceptual Clarity in NeuroAI

Abstract As neurotechnology and artificial intelligence increasingly converge in NeuroAI systems, concerns about fairness have become central to debates in AI ethics, neuroethics, and law. This article examines a recurring conceptual ambiguity in these debates: the tendency to treat AI algorithmic bias and neurodiscrimination as interchangeable. AI algorithmic bias concerns distortions in data, model design, or deployment that generate systematically unfair outputs. Neurodiscrimination concerns person-centred disadvantage based on neural characteristics or inferences drawn from neurodata. The article further examines the proposed neuroright to protection from algorithmic bias as an important site where this ambiguity becomes especially evident and consequential: although framed as a rights-based response, the proposed right often translates equality concerns into the language of technical system design. The article argues that treating these concepts as interchangeable obscures mechanisms of harm, misallocates responsibility, and misdirects regulatory responses. Through analysis of academic literature, neurorights frameworks, and policy documents, it shows how conceptual slippage could lead to mismatched interventions: technical auditing may be offered where rights-based protection is required, while individual remedies may leave biased systems uncorrected. The article concludes that conceptual clarity is not merely semantic. It is a necessary condition for coherent, accountable, and rights-respecting governance of NeuroAI systems.