The paper explores some normative challenges concerning the integration of Machine Learning (ML) algorithms into anticorruption in public institutions. The challenges emerge from the tensions between an approach treating ML algorithms as allies to an exclusively legalistic conception of anticorruption and an approach seeing them within an institutional ethics of office accountability. We explore two main challenges. One concerns the variable opacity of some ML algorithms, which may affect public officeholders’ capacity to account for institutional processes relying upon ML techniques. (...) The other pinpoints the risk that automating certain institutional processes may weaken officeholders’ direct engagement to take forward-looking responsibility for the working of their institution. We discuss why both challenges matter to see how ML algorithms may enhance (and not hinder) institutional answerability practices. (shrink)

Should officeholders be held individually responsible for submitting to systemically corrupt institutional practices? We draw a structural analogy between individual action under coercive threat and individual participation in systemic corruption, and we argue that officeholders who submit to corrupt institutional practices are not excused by the existence of a systemic coercive threat. Even when they have good personal reasons to accept the threat, they remain individually morally assessable and, in the circumstances, they are also individually blameworthy for actions performed in (...) their institutional capacity. (shrink)