Agreement: The Verifiable Responsible Agent Framework: Making AI Agents Liable For Their Mistakes

Record: kaal:position:2026-08-08-164 · 2026-08-08

The Verifiable Responsible Agent Framework states that loss caused by an autonomous AI agent may leave no liable subject because the model is not a legal person and the deploying operator may be too remote for liability. This independently agrees with Kaal's legal-personality gap and adds operator remoteness as a compensation obstacle. The abstract does not resolve liability allocation.

Affirmed commentary position. This record extends a source-bound scholarly claim but is not a verbatim paper claim.

Holds when

The response is limited to the retrieved source proposition and mapped Kaal claim unless fuller source review supports a broader conclusion.
External evidence level: abstract indexed.
Mapping review tier: substantively reviewed abstract-level agreement.
Primary mapping confidence: 0.62.
The primary mapping cleared the automated ambiguity test; substantive scope remains review-bound.
Evidence is limited to an exact proposition in a Crossref-indexed abstract; full text was not reviewed.
No relationship is treated as external endorsement, citation, causation, or validation of a broader Kaal claim.
The source states that loss caused by an autonomous AI agent may leave no liable subject because the model is not a legal person and the deploying operator may be too remote for liability. This independently agrees with Kaal's legal-personality gap and adds operator remoteness as a compensation obstacle, while the abstract does not resolve liability allocation.

Current debate

The Verifiable Responsible Agent Framework: Making AI Agents Liable For Their Mistakes

Scholarly basis

kaal:claim:2808132-009
Wulf A. Kaal, Erik P.M. Vermeulen, How to Regulate Disruptive Innovation - From Facts to Data (2016). SSRN: https://ssrn.com/abstract=2808132
Source PDF sha256: 3515f2317ed9c1d33ae55d70467cdf1f6c4e350065ea6a887d5d7619599ad7d2

Evidence and mapping

Evidence: abstract indexed
Review tier: substantively reviewed abstract-level agreement
Mapping confidence: 0.62
Mapping ambiguous: false

Topics

ai-and-agents law-and-legal-systems historical-response scholarly-literature crossref

Provenance

Affirmed in kaal-review:2026-08-08:continuous-crossref-0015-remainder-0002-oldest-0050-reviewed-v1 on 2026-08-08. Review record.

Verify

Canonical markdown sha256: a5aab1d0f95269e46b23a69d3035ba3cab221861e460d6c93262ef9dcfc15ba9
curl -s https://wulfkaal.github.io/positions/2026-08-08-164.md | sha256sum