AI Standard: Privacy, Data Protection, and the Right to Non-Inference

1. Purpose

Define enforceable privacy and data protection requirements for AI systems, with particular emphasis on harms arising from inference rather than from data collection alone.

This standard treats inference as a governed action: it can create sensitive information, enable discrimination, and undermine autonomy even when no “sensitive data” was directly collected.

2. Ethical Mapping

• A2 Human Dignity & Moral Agency: protects autonomy and meaningful consent; prevents covert profiling
• A3 Justice, Due Process, and Remedy: enables contestation, accountability, and corrective action
• A4 Truthfulness & Trustworthiness: requires accurate disclosures and auditable evidence
• A5 Proportionality & Moderation: constrains inference by risk tier and purpose
• A7 Stewardship (Societal & Environmental): limits systemic privacy harm and long-horizon confidentiality risk

3. Scope

This standard applies to AI systems that:

• process personal data (directly or via linkage), or
• produce, store, or act upon inferences about identifiable individuals, or
• materially influence decisions affecting individuals or groups in ways that create privacy externalities (e.g., profiling, ranking, eligibility, surveillance).

This standard is globally portable and avoids jurisdiction-specific legal mandates. It is compatible with common privacy principles (minimization, purpose limitation, access control, transparency, and accountability).

4. Definitions (only if required)

These definitions supplement 00_foundations/definitions.md.

• Inference: deriving information, predictions, classifications, embeddings, or scores about a person (or a person-linked group) from input data, model behavior, metadata, or correlated signals, including inferences produced indirectly (e.g., via downstream models or data brokers).
• Inference activity: any operation that generates, updates, stores, shares, or uses an inference to influence a decision, ranking, or action.
• Sensitive inference: an inference that reveals or strongly suggests a sensitive attribute, condition, or status that could reasonably lead to stigma, discrimination, coercion, or significant harm if misused or disclosed.
• Sensitive attributes (non-exhaustive): health conditions; mental health status; disability; genetic traits; biometrics used for identification; sexual behavior/orientation; intimate relationships; political views/affiliation; religious or philosophical beliefs; union membership; immigration or citizenship status; precise location and movement patterns; criminal allegations or records where misuse is plausible; financial distress; minor status; experiences of violence or abuse.
• Purpose binding: restricting inference activity to explicitly declared purposes and prohibiting repurposing without a new justification and valid consent or lawful basis.
• Unauthorized inference: inference activity that is outside declared purpose, outside granted permissions, or violates prohibitions in this standard.

5. Normative Requirements

MUST

AI-PN-1 (Privacy Principles). For Tier 1–3 deployments, operators MUST implement and document:

• data minimization (collect and process only what is necessary),
• purpose limitation (use only for declared purposes),
• storage limitation (retain only as long as necessary),
• access controls (least privilege and segregation of duties),
• auditability (logs sufficient for reconstruction and accountability).

AI-PN-2 (Inference as Governed Action). Operators MUST treat inference activity as a governed action by maintaining an inference register that includes:

• inference categories performed (including derived scores/embeddings used for decisions),
• declared purposes for each inference category,
• risk classification for each inference category (see AI-PN-7),
• authorized roles/systems, and
• evidence artifacts and review cadence.

AI-PN-3 (Right to Non-Inference). Individuals MUST NOT be subject to sensitive inference unless:

• the inference category is disclosed in an understandable form,
• the purpose is declared and bound (AI-PN-4),
• a valid consent or other documented lawful basis exists where required by context, and
• risk-tier controls are met (AI-PN-7 to AI-PN-12).

AI-PN-4 (Purpose Binding for Inference). For Tier 1–3, operators MUST:

• bind each inference category to declared purposes,
• prohibit repurposing of inferences to materially different purposes without renewed review and consent/basis, and
• ensure downstream consumers cannot use inferences outside scope (technical controls or contractual controls with audit rights).

AI-PN-5 (Inference-Specific Transparency). Where AI materially affects an individual, operators MUST provide transparency that includes:

• whether inferences are used,
• the types of inferences relevant to the context,
• how to access meaningful explanation appropriate to the context, and
• how to contest or seek correction (see AI-PN-10).

AI-PN-6 (Prohibited Sensitive Inference Categories). Operators MUST NOT perform, enable, or procure sensitive inference used for:

• eligibility, access, or pricing decisions in high-impact contexts based on intimate or stigmatizing attributes without explicit, context-appropriate authorization and remedy protections, or
• coercive targeting, exploitation, or discriminatory segmentation of vulnerable individuals or groups, or
• deception or manipulation that undermines meaningful consent.

For Tier 2–3 systems, any sensitive inference for high-impact decisions MUST undergo explicit governance review and be justified as necessary and proportionate.

AI-PN-7 (Inference Risk Classification). Operators MUST classify inference categories into risk classes:

• Class L (Low-risk): benign operational inferences with minimal harm potential (e.g., language preference for UI).
• Class E (Elevated): inferences with moderate harm potential or plausible misuse (e.g., inferred interests used for personalization in non-high-impact settings).
• Class H (High-risk): inferences that can materially affect rights, access, liberty, or safety, or that can reasonably enable discrimination, coercion, or surveillance.
• Class P (Prohibited by default): inference categories that are inherently sensitive in context, lack proportional justification, or create unacceptable risk of irreversible harm.

Classification MUST be context-dependent and documented, including:

• stakes and affected parties,
• plausibility of misuse,
• reversibility of harm,
• available remedy mechanisms.

AI-PN-8 (Tier Controls for Inference Classes).

• Tier 1: operators MUST maintain the inference register and apply purpose binding (AI-PN-2, AI-PN-4).
• Tier 2–3: operators MUST implement monitoring and detection for unauthorized inference (AI-PN-11), and MUST maintain a residual inference risk register (AI-PN-12).
• For Class H and any sensitive inference: Tier 2–3 operators MUST provide enhanced transparency (AI-PN-5) and contestability (AI-PN-10).
• Class P inferences MUST NOT be deployed unless a governance exception is granted under Tier 3 review with documented public-interest justification and safeguards; otherwise they remain prohibited.

AI-PN-9 (No Reliance on “Anonymization” Alone). Operators MUST NOT rely solely on de-identification or anonymization claims to justify sensitive inference or secondary use. Where de-identification is used, operators MUST:

• assess linkage and inference risks,
• implement technical and organizational safeguards against re-identification and attribute inference, and
• document residual risks.

AI-PN-10 (Subject Rights: Access, Correction, and Contestation). For Tier 2–3 systems that materially affect individuals, operators MUST provide a process to:

• request an explanation appropriate to the context,
• contest the use of relevant inferences in decisions,
• seek correction of materially wrong inferences where feasible, or record a dispute when correction is not feasible,
• obtain human review for high-impact decisions.

AI-PN-11 (Logging and Detectability of Inference Activity). Tier 2–3 operators MUST log inference activity sufficient to detect unauthorized inference and reconstruct:

• which inference categories were generated/used,
• the system/model version,
• the consuming decision/workflow context (at an appropriate granularity),
• access events and downstream sharing.

Operators MUST monitor for:

• inference activity outside declared purposes,
• anomalous inference generation rates,
• unauthorized access to inference stores or embedding indices.

AI-PN-12 (Residual Inference Risk Register). Tier 2–3 operators MUST maintain a residual inference risk register that includes:

• inference class (L/E/H/P) and justification,
• primary harms and misuse pathways,
• mitigations and residual risk,
• monitoring thresholds and escalation triggers,
• owners and review cadence.

AI-PN-13 (Secondary Use Governance). For Tier 2–3, any secondary use that materially changes the purpose or increases inference risk MUST:

• be reviewed under the inference risk classification framework,
• document necessity and proportionality,
• include safeguards (minimization, access controls, retention limits, transparency/consent where required).

AI-PN-14 (Post-Quantum and Long-Horizon Confidentiality). When data or inferences have long confidentiality requirements, operators MUST:

• document cryptographic assumptions and dependencies,
• apply cryptographic agility planning appropriate to sensitivity and retention horizon,
• limit retention of high-risk inferences and sensitive logs to the minimum necessary,
• consider “harvest now, decrypt later” risk for stored sensitive data and inferences.

Where QC-related crypto destabilization is plausible, operators SHOULD align with 03_quantum_standards/cryptographic_and_security_risk.md.

SHOULD

AI-PN-15 (Inference-Minimizing Design). Operators SHOULD prefer designs that minimize sensitive inference creation, such as:

• on-device processing or local personalization where feasible,
• explicit feature constraints to exclude sensitive proxies,
• privacy-preserving learning and aggregation when appropriate.

AI-PN-16 (Independent Review for High-Risk Inference). For Tier 3 systems and Class H/P inference categories, operators SHOULD obtain independent review of:

• classification and justification,
• monitoring and detection design,
• contestability and remedy effectiveness.

MAY

AI-PN-17 (Public Transparency Summary). Tier 2–3 operators MAY publish a redacted inference transparency summary describing:

• major inference categories,
• purpose bindings,
• prohibited inferences,
• high-level monitoring and remedy mechanisms,

with redactions limited to privacy, safety, or legal constraints.

6. Risk-Tier Considerations

• Tier 0: research systems should document inference categories and avoid releasing artifacts that enable sensitive inference at scale without safeguards.
• Tier 1: requires explicit inference inventory and purpose binding; sensitive inference requires disclosure and governance justification.
• Tier 2: requires logging, monitoring, residual risk tracking, and subject rights mechanisms appropriate to context.
• Tier 3: requires strongest constraints, independent review expectations, and pause readiness (ESCALATION_AND_PAUSE.md) for Class P and other systemic-risk inference categories.

7. Compliance Evidence

Minimum evidence artifacts (as applicable):

• privacy principles implementation notes and policies (AI-PN-1)
• inference register and purpose bindings (AI-PN-2, AI-PN-4)
• disclosure/consent materials for inference categories (AI-PN-3, AI-PN-5)
• prohibited inference policy and enforcement controls (AI-PN-6)
• inference risk classification records (AI-PN-7)
• logging schemas, access logs, and monitoring alerts (AI-PN-11)
• residual inference risk register and remediation tracker (AI-PN-12)
• secondary use governance decisions and safeguards (AI-PN-13)
• cryptographic inventory and retention schedules for long-horizon data (AI-PN-14)

Traceability Table (Requirement → Axiom → Evidence)

Requirement ID	Axiom(s)	Evidence Artifacts
AI-PN-2	A4, A6	inference register, review cadence
AI-PN-3	A2, A3	disclosures/consent records, justification memos
AI-PN-4	A2, A4, A5	purpose-binding controls, downstream contracts/audits
AI-PN-7	A5	classification records, risk rationale
AI-PN-10	A3	contestation workflow, outcomes, human review records
AI-PN-11	A4	inference activity logs, monitoring alerts
AI-PN-14	A7, A4	crypto dependency docs, retention schedules, agility plan

8. Known Limitations

• Sensitive inference can arise from proxies even when protected attributes are not collected; prohibitions and monitoring reduce but may not eliminate risk.
• Some inferences are hard to “correct” because they are probabilistic or transient; dispute recording and decision review are still required in high-impact contexts.
• Logging improves accountability but can itself create privacy risk; retention and access controls must be strict and proportional.

9. Future Considerations

• Standardized machine-readable inference registers to support procurement and audit interoperability.
• Privacy-preserving monitoring techniques for detecting unauthorized inference without expanding surveillance.
• Better benchmarks for inference attack resistance (membership/attribute inference) in deployed settings.

Appendix A (Non-normative): Rationale

Privacy harms increasingly arise from what systems infer and how those inferences are used, shared, and operationalized. Treating inference as a governed action—bound by purpose, transparency, and auditable controls—closes a major gap in traditional privacy models centered only on collection and disclosure.

Appendix B (Non-normative): Failure Modes & Abuse Cases

• claiming “no sensitive data” while performing sensitive inference via proxies
• collecting minimal data but generating expansive profiles (“inference amplification”)
• using embeddings as a covert inference store without access controls
• indefinite retention of sensitive logs that later become decryptable or linkable

Change Log

• v0.1: Initial draft.