The article proposes two instruments: the Supervised Intelligence Methodology (SIM), a five-stage process standard for AI-assisted legal reasoning, and the AI Reliance Test (ART), an ex post accountability mechanism for courts, regulators and professional bodies.
Abstract
This article argues that generative AI in legal reasoning exposes a process-governance gap rather than merely a technology-risk problem. Professional conduct rules, ethical guidance and risk-based regulation, including the EU Artificial Intelligence Act, increasingly require competence, verification and human oversight. They do not, however, sufficiently operationalise the cognitive sequence by which legal professionals should frame a question, use AI output, reconstruct doctrine, verify sources and exercise final judgment. Drawing on Mata v. Avianca in the United States, Gummadi Usha Rani v. Sure Mallikarjuna Rao in India and human-AI interaction research on automation bias and cognitive offloading, the article proposes two instruments: the Supervised Intelligence Methodology (SIM), a five-stage process standard for AI-assisted legal reasoning, and the AI Reliance Test (ART), an ex post accountability mechanism for courts, regulators and professional bodies. The core contribution is the doctrine of Cognitive Sovereignty, understood as a non-delegable obligation to preserve independent professional judgment. The framework connects legal ethics with institutional legitimacy, rule-of-law accountability and democratic trust in AI-mediated adjudication.
This qualitative study conducts a comparative document analysis of ten influential governance instruments issued by UNESCO, the OECD, the European Union, the Council of Europe, the United States National Institute of Standards and Technology, the United Kingdom, the Group of Seven, and Singapore.
Kwan-Hong Tan· Open Access Journal of Multi...· 0 citations
This study investigates the fairness risks and regulatory mechanisms of AI-assisted judicial decision-making by establishing an integrated framework that combines algorithm interpretability, data governance, bias mitigation, and human–machine collaborative control.
A feasibility-first framework for admissible-state reasoning within Deterministic Systems Intelligence (DSI) is developed, which distinguishes continuity with earlier DSI release-governance work from a further theoretical question: whether an institution is justified in exercising consequential authority through a prop...
The article proposes an accountable tax-AI architecture built on task classification, authoritative and time-stamped sources, explicit assumptions, traceable evidence, mandatory escalation, reason-giving, taxpayer contestability, and post-deployment audit that treats AI as an evidentiary and analytical assistant while...
Edvin Stefani· Open Journal for Research in...· 0 citations
The central claim is that constitutional and democratic requirements should not be treated as external compliance burdens when embedded into institutional design, they operate as productive constraints that improve legitimacy, implementation discipline, and the long-term trustworthiness of AI-enabled public decision-ma...
C. Oliveira· Open Access Journal of Data...· 0 citations
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