Decision Making in Asylum Law and Machine Learning

What is it about?

This article avails an autoethnography of the authors’ attempt to construct a post hoc intervention machine learning (ML) system responsive to the problem of discrimination in asylum law decisions. In the article we ask whether such a ML-driven post hoc intervention system reduces the overall risk of discrimination emerging from human discretion in legal decision making on asylum. We conclude that a ML-driven ‘anti-discrimination machine’ will displace rather than reduce that overall risk. We warn that similar attempts at using ML as part of legal decision making, decision support, and post hoc interventions, in international law and beyond, needs to take seriously the risks of human discretion embedded in ML design and data selection.

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