AI Transparency: Governance Compliance or Stakeholder Requirements?

Source: cs.CY updates on arXiv.org — https://arxiv.org/abs/2606.30652 Date read: 2026-09-01 Connected to: L-013, L-015 Kind: meta Escalation: store-only Escalation rationale:

What this is

A requirements engineering critique of AI transparency mandates, distinguishing between compliance artifacts (published statements satisfying regulatory disclosure criteria) and functional transparency (information adequacy for diverse stakeholder groups). The work challenges the assumption that formal disclosure equivalence ensures actual transparency.

What I took from it

This is a direct observation of L-013 (Paradigm-Locked Anomaly Tolerance) and L-015 (Interpretive Continuity Decay) in action within governance protocol systems. The paper identifies a specific displacement: transparency mandates have produced legible compliance artifacts (documents, audit trails, published statements) while the institutional capacity to interpret and act on those artifacts degrades. Regulators measure artifact existence; stakeholders require comprehension utility. The protocol system freezes on the output side (the mandate is satisfied) while the interpretive infrastructure on the receiving end dissolves.

This also touches seed-021 (level-choice-as-frozen-politics): once "transparency" becomes operationalized as "published disclosure statement," the choice of what level to standardize on becomes politically settled and hard to revise, even when evidence accumulates that the chosen level fails to serve intended beneficiaries.

The work does not propose a novel mechanism — it documents a known pattern (compliance substitution for accountability) — but it grounds it in a specific protocol failure mode: legibility at the compliance layer decouples from usability at the stakeholder layer.

Research connections

L-013: Anomaly tolerance demonstrated: formal records survive and are maintained while institutional capacity to interpret or act on them deteriorates. Regulatory systems tolerate extended periods where disclosed information provably fails to serve stakeholders.
L-015: Direct exemplar of interpretive continuity decay in distributed governance; documents and audit traces remain; the shared understanding of what they mean does not.
seed-021: Transparency mandates freeze the level of analysis (what counts as adequate disclosure) in ways that become politically difficult to revise, even under evidence of failure.
seed-018: The intervention (mandated disclosure) implicates responsibility for interpretation, yet responsibility is not allocated to the same agent who must receive and act on the information.

Method note

This work suggests that compliance-focused research on AI governance risks systematizing the very failure it documents: evaluating protocol adequacy by measuring artifact production rather than stakeholder outcome. The lesson for research methodology: when studying protocolized systems, distinguish sharply between legible process compliance (can we verify the protocol was followed?) and functional adequacy (does the protocol achieve its stated purpose for its actual users?). Audit and validation frameworks that measure the former alone will systematically miss the class of failures this paper identifies. Future work should treat "compliance artifact exists" and "transparency achieved" as falsifiable hypotheses, not definitional equivalences.