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Attestable

When probabilistic AI feeds safety-critical systems, unproven output can drive physical action unchecked. Attestable is a discipline for marking a claim's basis, so it cannot.

OT SecurityCyber-Physical AssuranceIEC 62443ProvenanceSafety CaseLLM Agents

Readings: Neighbouring Fields

4 entries

Readings on assurance cases, the provenance and policy fields, trust in automation, and testimony. Feeds the review chapter's fifth section and its coda.

  1. Reading: The Safety-Case Exclusion, Defended The problem statement set aside the safety-assurance literature. This entry earns that exclusion by engaging it, and its hardest case, assurance cases for machine learning. The field structures an argument over evidence whose standing it presupposes, about a component at design time, and names the admissibility gap only to hand it on.
  2. Reading: The Provenance and Policy Fields Widening two rivals from their specifications to their fields. Provenance, from its founding survey, records origin and feeds a trust judgement made elsewhere; its ML forms gesture at use but only in a document for a human. Policy evaluates supplied rules over supplied attributes and presupposes the model this work builds.
  3. Reading: Trust, Reliance, and Automation Bias Grounding the premise that the next owner relies on conveyed state and acts on it. The trust-in-automation field has studied over-reliance (misuse, automation bias, blind reliance) for decades, with fatal aviation cases where crews acted on conveyed state indistinguishable from correct. Its remedy, calibrated reliance, requires basis, but is addressed to a human.
  4. Reading: The Handover, in Epistemology The bounded coda. The review ends where its problem is oldest: the epistemology of testimony, whose transmission-versus-generation debate is the epistemology of the handover, and whose standard example is the promotion of a claim beyond its basis. Philosophy has the case but no name for the elevation; this work supplies it and departs to the engineering.