This checklist is designed to guide legal teams, compliance officers, and business leaders through building a robust AI governance framework. It provides a structured, pragmatic approach to developing policies, guidelines, and processes that identify risks, set guardrails, and build trust in AI adoption.
This section focuses on establishing the foundational elements of your AI governance framework, including the formation of a dedicated team and the definition of core principles.
[ ] Establish a Multidisciplinary AI Governance Team
Assemble a cross‑functional team from legal, compliance, data science, engineering, business units, and executive leadership to oversee AI strategy and ensure policy compliance.[1]
[ ] Define the Team’s Core Responsibilities
Outline responsibilities such as policy creation and implementation, employee training, vendor due diligence, and IP protection.[2]
[ ] Establish Values‑driven AI Principles
Define values‑driven principles for ethical and responsible AI, e.g., fairness, transparency, and privacy.[3]
[ ] Articulate Purpose and Intent
Identify AI goals, business drivers, and problems to solve. Align governance with business strategy.[4]
[ ] Conduct a Readiness Assessment
Evaluate current AI maturity, data infrastructure, and talent to inform the policy and roadmap.[5]
Essential components to include in the organization’s AI policy document.
[ ] General Policies for AI Use
Define acceptable use for AI systems, standards for data protection and information security, and safeguarding IP.[6]
[ ] Prohibited Use Policies
Prohibit privacy‑infringing, discriminatory, or misinformation‑promoting uses. Restrict unapproved surveillance or profiling and unauthorized external tool use.[7]
[ ] Incident Reporting Policies
Require immediate reporting of breaches, misuse, or malfunctions. Define mechanisms, escalation paths, and coordination with regulators and stakeholders.[8]
[ ] Legal and Regulatory Compliance
Ensure compliance with applicable laws and guidelines, including AI‑specific laws such as the EU AI Act, consumer protection, and privacy regulations. Follow guidance from bodies such as the FTC, EEOC, and SEC.[9]
Critical legal considerations related to data handling in the context of AI.
[ ] Data Rights and Privacy
Confirm training data is properly licensed and privacy‑compliant. Avoid unauthorized scraping and usage beyond privacy policies.[10]
[ ] Copyright and Infringement Risks
Determine copyright status or fair use. Secure licenses or assess risk when using protected content for training.[11]
[ ] Bias, Discrimination, and Data Quality
Evaluate datasets for representativeness and bias. Implement measures to promote fairness and avoid discriminatory outcomes.[12]