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AI + Quantum Ethics Standards

AI Standard: Transparency & Explainability

1. Purpose

Ensure AI systems can be understood, evaluated, and contested at levels appropriate to risk, while preventing deceptive or obscured operation.

2. Applicability

  • Applies to AI systems used in Tier 1–3 contexts; Tier 0 is encouraged as feasible.
  • Strongest requirements apply to high-impact decisions and Tier 2–3 deployments.

3. Ethical Mapping

  • A3 Justice: contestability and meaningful explanation
  • A4 Trustworthiness: truthful capability/limitation representation
  • A2 Dignity: transparency supports consent and agency

4. Requirements (Normative)

AI-T-1 (Disclosure Packet). For Tier 1–3 deployments, operators MUST maintain an AI system disclosure packet including:

  • intended purpose and prohibited uses
  • system boundaries and dependencies (data, models, services)
  • known limitations and uncertainty characterization
  • training / fine-tuning data provenance at an appropriate granularity
  • evaluation results relevant to the deployment context

AI-T-2 (Affect Notice). When an AI system materially influences an affected party, the operator MUST provide timely notice that AI influence occurred, unless doing so would create a demonstrable safety risk that is documented and mitigated.

AI-T-3 (Explanation Standard by Tier).

  • Tier 1: operator SHOULD provide an explanation suited to operational debugging and user understanding.
  • Tier 2: operator MUST provide an explanation sufficient for independent review and contestation by affected parties (directly or via representatives), including key factors and uncertainty.
  • Tier 3: operator MUST provide an explanation and evidence package sufficient for third-party audit, including causal analysis where feasible and comprehensive uncertainty treatment.

AI-T-4 (Explainability Debt Register). Tier 2–3 operators MUST maintain an “explainability debt” register that:

  • identifies components with low interpretability
  • documents compensating controls (testing, constraints, human oversight)
  • defines a time-bound plan to reduce debt or justify permanence

AI-T-5 (Anti-Deception). Systems MUST NOT intentionally misrepresent whether outputs are AI-generated, or the level of confidence/uncertainty, in ways that materially affect decisions.

5. Compliance Evidence

  • disclosure packet artifacts and version history
  • affected-party notice templates and delivery logs
  • explanation reports and appeal outcomes (Tier 2–3)
  • explainability debt register with mitigation tracking
  • product/UI audits for deception patterns

6. Rationale (Non-normative)

Transparency is not a single feature; it is a bundle of artifacts and practices enabling accountability, remedy, and safe operation. Explainability debt makes “we can’t explain it” a managed, measurable risk instead of an excuse.

7. Failure Modes & Abuse Cases

  • hidden model updates changing behavior without notice
  • “confidence theater” (fake certainty cues)
  • explanations that are technically correct but practically unusable by affected parties

8. Change Log

  • v0.1: Initial draft (filename: 02_ai_standards/transparency_and_explainability.md).