Justice in The Loop: Fairness and Legitimacy in AI-Assisted Courts

DOI

10.2991/978-2-38476-553-9_51How to use a DOI?

Keywords

Algorithmic Fairness; Bias in AI Systems; Inclusivity in Judicial AI; Human Oversight; Rule of Law and AI

Abstract

This article explores the integration of artificial intelligence (AI) into judicial systems and its implications, with particular attention to the risks of bias against transgender and non-binary litigants, as well as concerns surrounding procedural opacity. As certain jurisdictions begin embedding generative AI directly into court judgments, the paper traces its expanding role in mediation and decision-support tools, underscoring both the potential for increased efficiency and the emergence of systemic vulnerabilities. Drawing on established frameworks governing AI in judicial systems, this article offers a concise comparative analysis of the respective advantages and overlooked risks in European, Canadian, and U.S. approaches. It argues that current procedural safeguards may be insufficient to ensure fairness and accountability, advocating for the adoption of transparent AI models and the prioritization of traceable, explainable outcomes.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Allan Liao
PY  - 2026
DA  - 2026/03/25
TI  - Justice in The Loop: Fairness and Legitimacy in AI-Assisted Courts
BT  - Proceedings of the 2025 4th International Conference on Educational Science and Social Culture (ESSC 2025)
PB  - Atlantis Press
SP  - 445
EP  - 456
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-553-9_51
DO  - 10.2991/978-2-38476-553-9_51
ID  - Liao2026
ER  -