One key area of focus is the question of under what conditions AI systems enable responsible action. Particular attention is paid to explainable AI (XAI) in complex decision-making contexts, such as in Medicine. 

The research examines what form explanations must take so that they not only provide technical transparency but also serve as a basis for well-founded judgements and responsible decisions. In this context, explainability is understood as a relational and context-sensitive requirement that is oriented towards the epistemic and practical needs of different stakeholders. At the same time, it is assumed that the requirements for good explanations increase in line with the potential consequences: the more is at stake for those affected, the greater the burden of justification that an explanation must fulfil.

Digital systems classify, evaluate and categorise people. In doing so, they can reproduce existing inequalities or give rise to new forms of injustice. Research analyses these processes from a relational justice perspective. The focus is on algorithmically mediated forms of disadvantage, in particular discrimination. 

Particular attention is paid to the tensions between different demands for justice, such as the protection against marginalisation, the avoidance of essentialisation and the promotion of empowerment.

Digital technologies open up new avenues to social and cultural practices, but at the same time create new mechanisms of exclusion and exacerbate existing digital inequalities. The focus is on the conditions for successful participation. Participation is understood here not merely as access, but as equal involvement in social practices. The starting point is an analysis of digital inequalities (the digital divide), which manifest themselves not only in access to technologies, but also in skills, opportunities for use and social embeddedness. The research examines how digital infrastructures enable participation or structurally limit it.

Digital systems are embedded in institutional and social contexts and function as structuring frameworks for social practice. This research focus examines how platforms, data practices and algorithmic decision-making architectures can be designed to meet requirements of justice, transparency and accountability. The focus is particularly on conflicts of objectives and accountability, for example between informational privacy, individual and collective autonomy, and well-being. At the same time, the research examines how such normative requirements can be reconciled with forms of regulation that are conducive to innovation. 

Particular attention is paid to questions of attributing responsibility in complex, distributed socio-technical systems, especially in contexts of human-machine interaction, such as autonomous driving or care assistance systems. The aim is to develop an ethics that not only evaluates but also opens up concrete avenues for shaping the future.