Top 10 Generative AI Governance Best Practices

Generative artificial intelligence systems like chatbots and image generators powered by models such as DALL-E and Claude have exploded in capability and accessibility over the past couple years. While bringing many benefits, their ability to create authentic-looking but completely fabricated content also poses risks around misinformation, privacy violations, bias amplification, and more that require thoughtful governance.

Below are 10 best practices organizations should consider when deploying and managing generative AI systems:

1. Establish clear purposes and scope boundaries. Before deployment, document specific use cases, data types, output types, and access rules to constrain generative models to appropriate applications. Continuously reevaluate as capabilities advance.
2. Implement rigorous access controls. Control access through authentication, authorization levels, watermarking, mandatory disclosures, prompt engineering techniques, strict compliance monitoring, and limiting third-party integrations.
3. Maintain full records of system use. Comprehensive activity logs allow tracing outputs to inputs and users to enable auditing, trend analysis, oversight, and accountability. Logs should capture prompts, parameters, outputs, user details, and timestamps.
4. Apply robust validation processes. Rigorously validate outputs for accuracy, factual consistency, logical coherence, appropriate tone, legal/ethical compliance, and lack of potential harms before broad publication or use in sensitive contexts. Manual review is critical.
5. Engineer safety directly into systems. Build capabilities like Selective Question Answering into models that allow refusing dangerous, unethical, false, or poorly grounded prompts. Rate limiting can also slow viral misinformation spread.
6. Implement effective oversight procedures. Establish clear human and automated oversight procedures to regularly review system operations, audit logs, validate outputs, assess emerging risks, and continuously strengthen governance controls per evolving generative AI capabilities and threat models.
7. Cultivate a responsible AI culture. Provide extensive training to users on responsible, ethical application of systems. Cultivate organizational awareness of risks. Encourage reporting questionable uses without retaliation. Model an environment of trust and collective responsibility.
8. Mitigate biases through diverse data/teams. Address biases by ensuring training data diversity, filtering objectionable embedding data, and enabling diverse teams to participate in model development, governance enforcement, output validation, oversight procedures, and ethical reviews.
9. Maintain confidentiality and security. To build user trust and prevent harms from dataset exposure, ensure confidentiality preservation, system security, responsible data stewardship, and deletion options. Enable opt-out requests. Allow pseudonymous use where appropriate.
10. Plan for transparency and accountability. Disclose key model capabilities, limitations, data sources, errors, confidence estimates, risks, and uncertainty to set proper expectations around reliability. Enable review of potentially harmful content. Implement accessible appeal procedures for questionable outputs or bans. Welcome external auditing.

The pace of advancement in generative AI demands proactive governance centered on ethical principles of trustworthiness, transparency, accountability, reliability, safety, security, privacy, confidentiality, diversity, and responsible innovation. While no framework can fully eliminate risks, organizations who invest in continuous governance improvements will best position themselves to harness benefits while avoiding pitfalls as this extraordinarily powerful technology continues progressing.

Responsible AI Book