Audit of AI algorithms

1) What is an AI audit and why is it needed

An audit of AI algorithms is a systematic check of data, models, processes and controls that proves that AI works reliably, fairly, safely and legally, and risks are managed.

• Increase trust (stakeholders, customers, regulator).
• Reduce operational/reputational/legal risks.
• Ensure reproducibility and manageability of the life cycle (ML/LLM Ops).
• Reinforce business decisions with measurable quality and risk metrics.

2) Scope and boundaries of the audit

Level of data: collection/consent, quality, bias, privacy, lines of origin.
Model level: methodology, validation, explainability, robustness, vulnerabilities.
Product level: UX risks, person-in-the-loop, feedback and escalations.
Level of operations: monitoring, SLO, incidents, rollbacks, version management.
Legal and ethics: rights of data subjects, prohibitions/restrictions, documentation.
Suppliers and 3rd-party: external models, API, data, licenses, contract guarantees.

3) Risk-based methodology (skeleton)

1. Criticality of use: impact on finance/health/rights (low/medium/high).
2. Risk identification: data, fairness, security, privacy, hallucinations, abuse.
3. Controls and evidence: what mechanisms reduce risk and what artifacts confirm.
4. Scoring and scoring: scoring scales (0-3/0-5) by domain, "go/no-go" thresholds.
5. Remediation and improvement plan: SLA fixes, owners, deadlines.
6. Continuity: frequency of repeated audits, triggers of unscheduled inspection.

4) Documentation and artifacts (evidence)

Data Sheet: sources, schemes, rights and consents, cleaning, displacements, retention.
Model Card: purpose, training data, metrics, restrictions, safe use conditions.
Eval Report: offline assessment methodology, splits, bootstrap/CI, stress cases.
Risk Register: list of risks with probability/impact, remediation status.
Change Log: data/code/model/promptt versions, release dates.
Playbooks: rollback, escalation, DSAR/data deletion, incident response runbooks.
Supplier Dossier: terms of providers (LLM API, models), limits and guarantees.

5) Data audit

Legality and consents: legal grounds, purposes of processing, cross-border transfers.
Quality/trust: freshness, completeness, uniqueness, drift of distributions.
Bias: class imbalances, representativeness, proxy characteristics.
Privacy: pseudonymization/tokenization, differential privacy (if applicable), access logs.
Linage: tracing from the source to the showcases and the feature platform; reproducibility of datasets.
Licenses and IP: rights to learn/distribute derivatives.

Mini checklist: is there a glossary of metrics/fields, schema contracts, DQ tests, consent log, DSAR procedures?

6) Audit of classic ML models

Validation and retraining: correct splits, leakage checks, stability on time slices.
Robustness: stress tests (noise, emissions, omissions, shifts), adversarial samples in reasonable domains.
Fairness: disparate impact, equal opportunity, calibration parity; analysis by segment.
Explainability: local/global SHAP/ICE, stability of importance.
Application limitations: uncertainty zones, fallback logic, human-in-the-loop.
Quality economics: cost curves, error profiles, guardrail metrics.

7) LLM/Generative Systems Audit (optional)

Hallucinations and validity: proportion of responses with sources, factual evals.
Content security: filtering malicious/prohibited, protection against jailbreak/prompt-injection.
Context and leaks: restrictions in RAG (PII/secrets), policy on citation of sources.
Tools and functions: safe boundaries when calling functions (no DDL/DML, limits).
Behavior regressions: A/B by prompt sets, "freezing" of system instructions, prompt version.
Usability and ethics: refusal/redirection in risk cases, correct disclaimers, protection against automation of abuse.

8) Safety and operational risks

Model security: extracting training data, membership inference, model stealing - tests and guards.
Supply-chain ML: integrity of artifacts (models, weights, embeddings), signatures, dependency control.
Infrastructure: isolation of environments, secret management, egress control, quotas.
Observability: logs/metrics/tracing, drift and quality alerts, request/export audits.
Incidents: definition of "AI incident," RACI, notice periods, post-mortems.

9) Metrics and eval practices

Quality by task: accuracy/AUC/MAE/F1; для LLM — pass@k, faithfulness, groundedness.
Fairness: gaps by segment, equalized odds/TPR-gap, unfairness-score.
Robustness: drop in noise/shear metrics; worst-case by segment.
Security: jailbreak-rate, toxicity/abuse rate, data exfil success rate.
Economy: cost-to-serve, latency p95/p99, cache hit-rate, errors/1000 requests.
Trust and experience: complaints, appeals, share of manual overrides, reaction time.

10) Online monitoring and risk management

Drift detectors: population comparisons of features/predictions; alerts and auto-degradation.
Guardrails: ranges, confidence thresholds, block lists/allow lists.
Human-in-the-loop: in critical cases - mandatory verification, feedback training.
A/B and observed effects: linking model metrics to business metrics and guardrail KPIs.
Rollbacks and release contour: canary/blue-green, model/promptt/data version.

11) Compliance with regulations and internal policies

Privacy and rights of subjects: the right to access/remove/explain, retention, localization.
Transparency requirements: purpose, contact for appeals, restrictions.
AI risk management: registration of high-risk systems, impact assessment (AIA/PIA), periodic reviews.
Contracts and SLAs with vendors: export logs, processing location, sub-processors, audit rights.

12) Roles and responsibilities

AI/ML Owner: Model Owner and Quality.
Data Steward: data owner and DQ/lineage.
Risk & Compliance: policy, checks, interaction with the regulator.
Security/Privacy: access control, attack/leak tests.
Product/UX: risk-based interface and content design.
Audit Lead (external/internal): independent evaluation and report.

13) Solution Tools and Classes

DQ/catalog/lineage: quality tests, lineage, glossaries, kit passports.
Evals and test kits: offline/online assessment, stress case generation, benchmark kits.
LLM security: prompt-injection scanners, content filters, policy-checkers.
Monitoring: telemetry of inference, drift detectors, audit of actions/exports.
Prompts/models management: registers, version control, reproducibility.
Red Team platforms: attack catalogs, scenarios, automatic tests.

14) Antipatterns

Accuracy only: ignore fairness/robustness/privacy/security.
No documentation: Missing Model Card, Data Sheet, change log.
Raw PIIs in LLM features/context: leaks and legal risks.
Lack of online monitoring: the event happened - no one noticed.
Opaque UX: the user does not understand what AI is and how to challenge.
One-time audit: no cycling and revision triggers.

15) Audit Implementation Roadmap

1. Foundation: AI policy, role model, Risk Register, Model Card/Data Sheet templates.
2. Data control: contracts, DQ tests, lineages, licenses and consents.
3. Eval-frame: quality/fairness/safety metrics, sets of stress cases.
4. LLM hygiene: RAG policies, filters, injection protection, source log.
5. Monitoring and incidents: telemetry, alerts, kickbacks, runbooks, staff training.
6. External readiness: reporting to regulator/clients, independent audit of high criticality.
7. Continuous improvement: retro cycles, budget guards, regular red team sessions.

16) Pre-launch AI model/function checklist

• Data Sheet and Model Card filled in; rights/licenses confirmed.
• Conducted evals: quality, fairness by segment, robustness, safety.
• For LLM: hallucination/groundedness measurements; protection against prompt-injection/jailbreak.
• Monitoring and alerts (quality, drift, toxicity, latency/cost) are installed.
• There is a human-in-the-loop and appeals process for critical decisions.
• DSAR/removal/retention are described and tested on the stage.
• Model/Prompt Register updated; ready rollbacks and canary.
• Security-review and red teaming conducted; eliminated blocking findings.

17) Example of the structure of the audit report (skeleton)

1. Summary and risk scoring (table by domain).
2. System description (purpose, users, context).
3. Data (sources, rights, quality, offsets, lines of origin).
4. Model/LLM (architecture, training, metrics, constraints).
5. Security/privacy (controls, attack tests, access log).
6. Eval results (quality, fairness, robustness, safety, UX).
7. Operations (monitoring, SLO, incidents, rollbacks).
8. Compliance (policies, processes, artifacts).
9. Violations/gaps and remediation plan (SLA, owners).
10. Applications: Model Card, Data Sheet, logs of experiments, versions.

18) Mini-templates (pseudo-YAML)

Model Card (short)

yaml model:
name: churn_xgb_v12 purpose: owners customer outflow forecast: [data_science@company]
data:
sources: [events_app, payments, support_tickets]
rights: consent:true; pii:tokenized evals:
metrics: {auc: 0. 86, f1: 0. 62}
fairness: {tpr_gap_gender: 0. 03}
limits:
do_not_use_for: credit decisions operations:
monitoring: {drift: enabled, latency_p95_ms: 120}
rollback: canary -> blue_green

LLM Guardrails

yaml llm:
blocked_content: [pii, sexual, violence, illegal_advice]
tools_allowlist: [sql_read_analytics, search_docs]
max_tokens: 1024 require_sources: true pii_redaction: on injection_scan: on

19) The bottom line

An audit of AI algorithms is not a one-time "tick," but a continuous process of risk management along the entire chain of data and models: from consents and biases to hallucinations and incidents. When documentation, eval framework, operational controls, and transparent UX work together, AI becomes a reliable, verifiable, and cost-effective product component.