American Journal of Advanced Multidisciplinary Innovation and Research

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Legal Accountability in Artificial Intelligence–Supported Professional Decisions

Author(s) Dr. Claire Dubois
Country United States
Abstract Artificial intelligence is increasingly incorporated into professional decision processes in medicine, law, finance, engineering, employment, public administration, and other high-consequence environments. AI-supported decision systems can improve information retrieval, pattern recognition, prediction, document analysis, prioritization, and workflow efficiency, yet their integration creates a persistent accountability problem: when professional judgment is influenced by an algorithm, responsibility may become distributed among the individual professional, employing organization, AI deployer, software provider, data supplier, and other actors. This paper examines how legal accountability can be preserved when artificial intelligence supports but does not formally replace professional decision authority.
It distinguishes mere human presence from meaningful professional control and develops an accountability framework based on competence, independent review, authority to override, traceability, documentation, auditability, risk escalation, and access to contestation and remedy. The contemporary regulatory environment increasingly reflects these principles. The European Union Artificial Intelligence Act establishes human-oversight and governance requirements for high-risk systems, while the July 2026 AI Omnibus extended application of Annex III high-risk requirements to December 2, 2027 and product-embedded high-risk requirements to August 2, 2028. NIST, UNESCO, OECD, Council of Europe, ISO, professional legal-ethics guidance, and healthcare regulation similarly emphasize governance, human responsibility, traceability, and risk management.
A synthetic evaluation of 480 hypothetical professional decisions compares four control conditions: uncritical acceptance of AI output, ordinary human review, independent documented professional review, and an integrated governance model combining review, audit, escalation, and appeal. The simulated composite accountability score rises from 44.2 to 61.5, 79.6, and 89.2 respectively. These values are methodological illustrations rather than empirical findings. The study concludes that accountability should attach not simply to the nominal presence of a human decision-maker but to whether that person possesses the knowledge, information, authority, time, and institutional support necessary to exercise genuine professional judgment.
Keywords artificial intelligence, legal accountability, professional responsibility, AI-assisted decision-making, human oversight, algorithmic accountability, professional negligence, explainable AI, auditability, high-risk AI
Field Engineering
Published In Volume 2, Issue 6, November-December 2021
Published On 2021-11-16
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