The Reflexive Local AI (RLAI) Framework: a methodology for AI-assisted transcription in empirical legal research

Abstract

Automated transcription has been increasingly adopted to relieve the burden of manual transcription in qualitative research, including empirical legal scholarship. Yet cloud-based commercial transcription services raise serious data security and confidentiality concerns, particularly where terms of service reserve rights to use uploaded content for model training. Existing scholarship, notably Da Silva’s work extending Bokhove and Downey, has identified these vulnerabilities and responded with a risk management framework grounded in security practice. This article departs from that framing. It grounds the case for local processing not merely in prudent risk mitigation, but in the specific legal architecture of UK data protection law, arguing that local processing of interview data may constitute a necessary technical and organisational measure under Article 32 UK GDPR, in the absence of a vetted, institutionally governed processing arrangement. Drawing on the author’s experience conducting thirty-three elite interviews for a British Academy/Leverhulme-funded project on pension governance and climate risk, this article develops that legal analysis into the Reflexive Local AI (RLAI) framework: five interlocking principles addressing local processing, human interpretive oversight, contextual anonymisation, transparent documentation and reflexive engagement. The article situates this framework within the methodological literature on transcription as constructivist interpretation, before analysing its legal foundations under Articles 5, 28 and 32 UK GDPR and Chapter V’s international transfer regime. It concludes by identifying the framework’s practical and empirical limits, and by proposing directions for its extension beyond transcription into AI-assisted qualitative analysis.