Ethical Frameworks for AI Hallucination Mitigation in Critical Applications (Journal of responsible AI & ethics, 2026)
Artificial Intelligence (AI) hallucination—the generation of factually incorrect or fabricated information with high confidence—poses severe risks in critical domains such as healthcare, defense, law, finance, autonomous systems, and public governance. Unlike traditional ML errors, hallucinations are linguistically fluent, contextually believable, and often indistinguishable from verified facts, increasing the probability of misinformation, unsafe actions, legal liability, economic loss, and ethical harm. This research investigates ethical governance frameworks, audit mechanisms, detection strategies, mitigation pipelines, and responsibility models to constrain and counter AI hallucination in mission-critical deployments. A multi-layer methodological approach involving domain risk modeling, ethical compliance benchmarking, human-AI accountability mapping, stakeholder surveys (n=620), hallucination stress-testing on generative models, uncertainty calibration metrics, and simulated high-stakes decision tasks was used for empirical validation. Findings reveal that 68% of hallucination incidents in critical AI responses go unnoticed when uncertainty scoring is absent, 74% of users overtrust fluent but incorrect AI outputs, and 81% of institutions lack enforceable accountability chains for AI misinformation. To address these failures, the study proposes SAFE-AI, a structured ethical governance blueprint integrating Source-Anchored Generation, Adaptive Fact-Verification, Failure Documentation, Explainability Binding, and Ethical Kill-Switch Protocols. Implementing SAFE-AI improved hallucination detection by 64%, factual traceability by 72%, human oversight efficiency by 59%, and ethical compliance audit scores by 67%. The study concludes that hallucination mitigation is not a technical optimization problem alone, but a governance and ethical systems engineering challenge requiring regulatory enforcement, epistemic traceability, uncertainty signaling, decision reversibility, and legally aligned accountability specification.