Oriol Pujol Vila

This paper explores the impacts of artificial intelligence (AI) systems on human autonomy and identifies the responsibilities of different stakeholders in addressing them. As AI systems increasingly shape not only the outcomes but also the conditions of everyday decision making, they directly influence individuals’ ability to exercise free and informed choice, a capacity central to democratic participation. Hence, it becomes crucial to examine how the definition, design, and implementation of AI systems may effectively support or constrain autonomy, through a well-defined organizational governance framework. Yet, most existing governance approaches overlook or marginalize the role of autonomy, leaving a critical gap. To address this, we introduce a governance framework structured around a set of diagnostic questions that link design choices to concrete dimensions of human autonomy at both system and user levels, and that map these impacts across the different stages of the decision-making process. The framework clarifies how responsibility for answering these questions can be distributed across different organizational roles, promoting internal reflection and collaboration. A dedicated section illustrates how the framework can be applied in practice to recognize and address autonomy impacts in AI systems within different organizational settings and development processes. This paper provides a practical tool to support autonomy-centred AI governance, grounded in the view that upholding human autonomy today is essential to secure sustainable and democratic futures tomorrow.

Este artículo aborda el concepto de discriminación algorítmica desde una perspectiva conjunta de la filosofía y la ciencia de la computación, con el propósito de establecer un marco de discusión común para avanzar en el despliegue de las inteligencias artificiales en las sociedades democráticas. Se presenta una definición no normativa de discriminación y se analiza y contextualiza el concepto de algoritmo usando un enfoque intencional, enmarcándolo en el proceso de toma de decisiones e identificando las fuentes de discriminación, así como los conceptos detrás de su cuantificación para terminar exponiendo algunos límites y desafíos.