Towards Just AI: Challenges and Solution Framework for Algorithmic Discrimination in Judicial System

Abstract With the rapid development of technologies like big data, artificial intelligence (herein after AI), and blockchain, society is ushering into a new era of digital civilization. However, the same algorithms that assist in efficient decision-making for human society may also introduce issues of algorithmic discrimination. Therefore, this paper focuses on the judicial domain to deeply explore potential instances of algorithmic discrimination in AI. It identifies three key dimensions of algorithmic discrimination risks in the judicial AI domain: the ambiguity of algorithm usage boundaries, the diversity in discriminatory outcomes, and the injustice within the algorithmic environment. Building upon this, the author examines global AI governance landscapes to extract corresponding governance strategies and practical insights. Finally, a systematic regulatory approach for addressing algorithmic discrimination in judicial AI is proposed, unfolding across three levels and nine aspects: making algorithmic limitations explicit, diversifying algorithmic regulations, and justifying the algorithmic environment. This framework aims to contribute to a more reasonable, systematic, and just governance of algorithms in judicial AI.