Justice by humans, assisted by AI
What should the future of adjudication look like?
DOI:
https://doi.org/10.35295/osls.iisl.2756Keywords:
algorithmic adjudication, online dispute resolution, generative AI, procedural justice, due process, machine learning, legal accountability, comparative lawAbstract
Algorithms have moved from the filing room to the bench. Machine learning, large language models, and online dispute resolution now shape bail, sentencing, and pleadings faster than the law can settle what this means for a fair hearing. Examining five jurisdictions across four continents, 2015–2025—47 statutes and judgments, 142 studies, seven datasets—this article finds three faults. Proprietary models and hidden training data leave the party bound by a decision unable to see its reasoning. Prediction displaces explanation: what usually happens stands in for why this case is decided. Risk falls on the litigants least able to absorb error, not on the institutions deploying the systems. Against Zeleznikow’s warning that prediction is not judgment, it proposes a duty to explain, a pre-deployment legal impact assessment, and an audit standard for fair process. The aim is not to keep artificial intelligence out of the courtroom, but to keep judgment human.
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