Resources

Practical, reusable materials for teams building and operating AI systems responsibly. Everything here is non-commercial and implementation-oriented — use as-is or adapt to your context. No fluff, no vendor pitches. Just tools that work.

Quick Start

Setting up a baseline responsible AI program? Begin with these five artifacts:

• Release Readiness Checklist (LLM/AI Products) — a lightweight pre-launch review covering privacy, security, safety, and fairness. Run it before every release; skip it at your own risk.
• Model Card Template — document intended use, known limitations, evaluation results, and risk areas. If your model doesn't have one, it should.
• Data Access & Retention Policy (Template) — defines who touches what data, for what purpose, and for how long. Plain language, ready to customize.
• Incident Response Playbook (AI-Specific) — step-by-step guidance for handling harmful outputs, data leaks, and misuse reports. Because "we'll figure it out later" isn't a plan.
• Glossary: Core Terms — a compact reference so your team speaks the same language when discussing fairness, safety, and governance.

Checklists

Short, structured reviews designed for three moments: before launch, during changes, and as part of ongoing governance.

Product & Engineering

• AI/LLM Release Readiness — covers privacy, security, safety, fairness, and transparency in one pass.
• RAG & Agents Checklist — source quality, access controls, prompt and log hygiene, evaluation coverage.
• Logging & Telemetry Review — PII minimization, retention windows, redaction rules, access restrictions.

Data

• Dataset Intake — provenance, consent, licensing, representativeness. The questions to ask before any dataset enters your pipeline.
• Data Minimization — collect only what you need. Define deletion paths before you collect, not after.
• Access Control — least privilege, audit trails, secrets management. Simple principles, often ignored.

Risk & Governance

• Risk Assessment Snapshot — impact, likelihood, mitigations, owner, review date. One page, updated quarterly.
• Vendor/Provider Review — evaluation criteria for third-party models, tools, and data processors.

Policy Templates

Copy-and-adapt documents for internal alignment. Written in plain language on purpose — legal polish can come later. Getting teams aligned comes first.

• AI Use Policy (Internal) — approved use cases, prohibited uses, review and escalation process.
• Data Retention Policy — retention periods, deletion workflows, exceptions and their justifications.
• Access Control Policy — roles, approval flows, audit logging, incident escalation paths.
• User Data Handling Notice (Plain Language) — what you collect, why, and how users can exercise control. Written for humans, not lawyers.
• Evaluation & Monitoring Policy — what you measure, how often, and what thresholds trigger action.

Curated Glossary

Recurring terms used across playbooks and templates. One definition each — enough to align a team without drowning in academic debate.

Privacy

• PII — information that can identify a person directly or indirectly.
• Purpose Limitation — data used only for stated, legitimate purposes.
• Data Minimization — collect and retain only what is necessary for the defined purpose.

Safety & Security

• Threat Model — a structured view of who might attack, what they want, and how they could succeed.
• Data Leakage — sensitive information exposed through logs, prompts, model outputs, or storage.
• Redaction — removing or masking sensitive fields before storage or sharing.

Fairness

• Proxy Variable — a feature that indirectly encodes a sensitive attribute (zip code as a proxy for race, for example).
• Disparate Impact — outcomes that disproportionately affect a protected group, even without explicit intent.
• Calibration — predicted probabilities that match real-world frequencies across groups.

Governance

• Human Oversight — defined points where humans can review, intervene, or stop an automated process.
• Accountability — named owners for risk decisions and mitigation follow-through. No ownership, no responsibility.

Reading List (Primary Sources)

A curated starting point for teams who want depth beyond checklists. Focus is on standards, frameworks, and widely cited guidance — not opinion pieces.

• Standards and risk management frameworks (ISO/IEC 42001, NIST AI RMF)
• Privacy principles and data protection guidance (GDPR core texts, OECD Privacy Framework)
• Documentation practices — model cards, dataset documentation, system-level transparency
• Safety evaluation and monitoring approaches — red teaming methodologies, benchmark suites, drift detection

Resource Index

Resource	Type	Best For
Release Readiness (LLM/AI)	Checklist	Pre-launch review
Model Card	Template	Documentation & transparency
Data Retention	Policy	Privacy & compliance hygiene
Incident Response (AI)	Playbook	Handling harmful outputs and misuse
Core Glossary	Reference	Shared vocabulary across teams

Updates & Contributions

Resources evolve. If you spot an error, want to suggest a primary source, or propose a new checklist or template — use the Contacts page. Include a reference and a short rationale when possible. Updates are logged and dated.