arXiv:2606.19407v1 Announce Type: cross Abstract: Large language models can produce fluent root cause analyses, but fluent final answers alone are insufficient evidence for accountability in high-stakes operations. In real incident response, engineers need to know what evidence supported a diagnosis, which alternatives were considered, where contradictions remained, and whether the system resolved the case or preserved uncertainty. We address this gap with JustDiag, a diagnostic justification engine for RCA that maintains an explicit process state over evidence, findings, competing hypotheses,
The increasing deployment of large language models in critical operations necessitates robust accountability frameworks to ensure reliability and trust.
Sophisticated readers will recognize that 'black-box' AI solutions are insufficient for high-stakes environments, demanding transparent justification for AI-driven diagnoses and decisions.
The development of diagnostic justification engines like JustDiag moves AI from merely providing fluent answers to offering accountable, evidence-backed root cause analyses.
- · AI accountability platforms
- · High-stakes industries (e.g., aerospace, healthcare)
- · Incident response teams
- · Opaque AI solution providers
- · Organizations relying solely on unverified AI outputs
Improved trust and adoption of AI in critical infrastructure and decision-making processes.
New regulatory standards and compliance requirements for AI-driven diagnostic systems.
A shift in competitive advantage towards companies that can provide verifiable, explainable AI solutions over those offering only performance metrics.
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Read at arXiv cs.AI