"Automating Anticorruption?"

Article Article scientifique Article "Automating Anticorruption?" eng 2022-11-09 2022-11-09 author Ceva Emanuela 0000-0002-4422-7667 927123 author Jimenez Garcia Maria Carolina 586401 48 10.1007/s10676-022-09670-x https://link.springer.com/article/10.1007/s10676-022-09670-x The paper explores some normative challenges concerning the integration of Machine Learning (ML) algorithms into anti- corruption 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. Corruption Anticorrution Accountability Artificial Intellingence Machine Learning Political Theory Political Philosophy Algorithms Dewey 320 121 Département de science politique et relations internationales T3 false Ethics and Information Technology 24 8 f0eba79a-2a6a-4604-84c0-4cdef8c92bc4 Article (Published version) Public Automating_Anticorruption.pdf 724283 application/pdf CC BY openAccess Faculté des sciences de la société / Département de science politique et relations internationales

Title	Published in	Access level	OA Policy	Year	Views	Downloads

"Automating Anticorruption?"	Ethics and Information Technology			2022	170	54

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Jimenez Garcia, Maria Carolina