This talk will introduce the concept of democratic trustworthiness of AI, shifting the focus from thinking about AI users to protecting citizens; and from output expectations to protected interests within AI systems. Most contemporary debates on Trustworthy AI (TAI) emphasise technical and output-oriented criteria such as accuracy, robustness, safety, and lawfulness. While necessary and valid, this talk argues that these markers are insufficient from a strictly democratic perspective on citizen interests. Drawing on Roger Hardin’s concept of trustworthiness as “encapsulated interest,” Eugenia reframes trustworthiness in AI as a relational and political question: whose interests are structurally taken into account by AI systems? Can everyone equally afford to trust AI, independent of its reliability?

The central claim is that dominant TAI frameworks tend to adopt a perspective in which the trustworthiness of AI is more about accurate information and less about power and representation. Trustworthiness is treated as a property of systems or outputs rather than as a socially distributed quality that varies across citizen groups. This obscures how AI systems systematically exploit the epistemic vulnerability of some, particularly marginalised citizens. Yet empirical research on algorithmic discrimination shows that women, people of colour, and non-binary people are disproportionately affected. Keeping this in mind, Eugenia therefore argues that AI should only be considered trustworthy if it demonstrably encapsulates the interests of those affected by it, especially those of vulnerable groups.