The Hallucination Defense

Responsibility and the “Hallucination Defense”

• Many commenters dismiss “the AI hallucinated” as a non-defense: tools don’t carry liability; users and their employers do.
• View: if you benefit from an AI tool, you also own the risk; using a non-deterministic system without proper controls is negligence.
• Others stress the real problem is evidentiary: everyone agrees some human is responsible, but it can be hard to prove who authorized what, under which constraints, and with what intent.

Legal Analogies and Edge Cases

• Comparisons are made to cars, dogs, toxic paint, spreadsheets, bots on the dark web, and “bricks on accelerators.” In nearly all analogies, liability falls on the human who chose, configured, or deployed the tool.
• Some note existing doctrines (vicarious liability, negligence, strict liability) already handle “my tool/employee did it” scenarios, including in finance and safety-critical domains.
• Others push corner cases: agents chaining actions, unexpected behaviors several hops away, or bizarre accident-style hypotheticals to probe where human liability might become ambiguous.

Logging, Warrants, and Authorization Chains

• The article’s proposal (cryptographically signed “warrants” that track scope and delegation between agents/tools) is seen as:
  • Useful by some for proving which human explicitly authorized a class of actions, especially in multi-agent systems.
  • Redundant or overengineered by others, who argue robust logging, access controls, and existing GRC practices are enough.
• Supporters emphasize warrants as an enforcement primitive (fail-closed authorization) whose audit trail is a byproduct, not just extra logs.

Skepticism and CYA Concerns

• Several see the whole idea as a CYA mechanism and “accountability sink” for management to scapegoat lower-level staff when AI-driven systems misbehave.
• Some criticize the article as misunderstanding when liability attaches and overhyping a not-actually-novel legal problem.

Broader AI Use and Reliability

• Strong consensus that LLMs hallucinate by design; they should not be used where high-stakes accuracy is required without human review.
• Some argue over whether punishment and personal responsibility should remain central, versus moving toward systems that emphasize prevention and self-correction over blame.