ai-model-compliance
Apply compliance controls to AI and automated decision systems — transparency, fairness, human oversight, documentation, and regulatory alignment. Use when deploying AI in regulated processes (credit, fraud, customer decisions) or responding to AI governance expectations.
- License 许可
- MIT
- 最近更新
- 2026-07-22
- Stars
- ★ 7
- Forks
- 1
01 前言 Front Matter
- name
- ai-model-compliance
- description
- Apply compliance controls to AI and automated decision systems — transparency, fairness, human oversight, documentation, and regulatory alignment. Use when deploying AI in regulated processes (credit, fraud, customer decisions) or responding to AI governance expectations.
03 SKILL.md 正文
AI / Model Compliance
Overview
AI compliance extends model and conduct obligations to machine-assisted decisions. It requires inventory, explainability proportionate to impact, monitoring for harmful outcomes, and clear human accountability.
When to Use
- AI in credit, fraud, AML, marketing eligibility, or customer treatment
- AI governance policy and control design
- Regulatory or audit inquiries on automated decisions
- Vendor AI / LLM due diligence
Core Practices
- Inventory AI systems and classify by decision impact
- Document purpose, data, limitations, and human oversight model
- Assess fairness and harmful bias where decisions affect individuals
- Monitor outcomes, drift, and incident patterns in production
- Ensure customers can obtain required explanations/appeals where mandated
- Contractually control vendor AI roles and data use
Principles
- High-impact decisions need stronger governance than copilots
- “The model did it” is not an accountability answer
- Transparency requirements vary by use case and jurisdiction — design for the strictest material case you operate in
- Testing before launch is necessary; monitoring after launch is mandatory
Verification
- [ ] High-impact AI systems are inventoried and owned
- [ ] Oversight, monitoring, and challenge processes exist
- [ ] Documentation supports exam and customer-facing duties
04 同仓库更多 Skills
compliance-monitoring
Design compliance monitoring and testing programs that verify controls work in practice and surface issues before regulators do. Use when building monitoring plans, compliance testing, or continuous control monitoring.
complaints-handling
Operate complaints-handling processes that meet regulatory standards — logging, timely resolution, root-cause analysis, and fair outcomes. Use when designing or improving regulated complaints frameworks or analyzing complaint themes for conduct risk.
aml-kyc-basics
Apply AML/KYC fundamentals — customer due diligence, monitoring, suspicious activity escalation, and recordkeeping — for financial crime compliance. Use when designing onboarding controls, transaction monitoring concepts, or AML program components in financial services.
data-quality-management
Manage data quality end-to-end — dimensions, rules, remediation, and prevention — beyond one-off cleaning scripts. Use when building sustained data quality programs or fixing systemic accuracy, completeness, or consistency problems.
data-lineage
Capture and use data lineage — where data comes from, how it transforms, and what depends on it — for impact analysis, debugging, and trust. Use when tracing metric origins, assessing change impact, or diagnosing broken pipelines.
data-governance
Establish data governance — ownership, policies, decision rights, and accountability — so data is trusted, compliant, and usable across the organization. Use when designing governance operating models, assigning data owners, or fixing chronic trust and access problems.