Relational accountability in AI-driven pharmaceutical practices: an ethics approach to bias, inequity and structural harm

Abstract

The integration of artificial intelligence (AI) into pharmaceutical practices raises critical ethical concerns, including algorithmic bias, data commodification and global health inequities. While existing AI ethics frameworks emphasise transparency and fairness, they often overlook structural vulnerabilities tied to race, gender and socioeconomic status. This paper introduces relational accountability-a feminist ethics framework-to critique AI-driven pharmaceutical practices, arguing that corporate reliance on biased algorithms exacerbates inequalities by design. Through case studies of Pfizer-IBM Watson's immuno-oncology collaboration and Google DeepMind's National Health Service partnership, we demonstrate how AI entrenches disparities in drug pricing, access and development. We propose a causal pathway linking biased training data to inequitable health outcomes, supported by empirical evidence of AI-driven price discrimination and exclusionary clinical trial recruitment algorithms. Policy solutions, including algorithmic audits and equity-centred data governance, are advanced to realign AI with the ethical imperative. This work bridges feminist bioethics and AI governance, offering a novel lens to address structural harm in healthcare innovation.

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