Ethical Implications of AI-Powered Courtroom Transcription Systems

Abstract

Artificial intelligence-powered courtroom transcription systems are transforming the production, retrieval, and management of judicial records by converting spoken proceedings into searchable digital text. Although automatic speech recognition offers substantial potential for improving transcription speed, accessibility, and administrative efficiency, its adoption within judicial environments raises significant ethical concerns because transcription errors can influence evidentiary interpretation, appellate review, litigant rights, and public trust. This study examines the ethical implications of AI-assisted courtroom transcription through a simulation-based analytical framework emphasizing accuracy, linguistic fairness, procedural justice, human oversight, privacy, accountability, and contestability. A synthetic audit of 800 courtroom-style transcription instances was designed across four speech conditions: Mainstream US English, African American English, South Asian-accented English, and disfluent speech. Word Error Rate and critical legal transcription error were used as principal performance indicators. The simulated results demonstrated substantial variation in error patterns, with higher modeled error rates under linguistically and acoustically complex conditions.

 Logistic regression indicated that increasing Word Error Rate and overlapping speech were associated with elevated probabilities of legally consequential errors, whereas structured human verification substantially reduced modeled risk. The paper proposes a Human-Accountable Courtroom Transcription Governance Model in which artificial intelligence  produces provisional rather than automatically authoritative records. Responsible implementation requires subgroup performance audits, secure audio governance, uncertainty disclosure, traceable corrections, professional human verification, meaningful appeal mechanisms, and periodic institutional auditing.