11 AI governance

As AI innovation is racing ahead, the risk surface of digital products is increasing and sometimes getting out of control. New cybersecurity threats, privacy violations, unfair outputs, and the black-box character of modern AI models can damage user trust and harm adoption, and regulators are constantly on the lookout for ways to constrain the use of AI. As a product manager, you have an important role in governing AI, bridging technical development, business objectives, compliance, and ethical considerations. By proactively addressing governance risks, you can build trust, drive responsible innovation, and position your AI for long-term success.

11.1 Security: Protecting sensitive assets

11.1.1 Data security

11.1.2 Model security

11.1.3 Usage security

11.2 Privacy: Maintaining trust through transparency

11.2.1 Managing privacy in the context of generative AI

11.2.2 Incorporating privacy-by-design

11.2.3 Regulatory context

11.3 Mitigating bias in AI systems

11.3.1 Training data bias

11.3.2 Algorithmic bias

11.3.3 Feedback loop bias

11.3.4 Regulatory context

11.4 Providing transparency

11.4.1 Explainability: Showing how AI makes decisions

11.4.2 Interpretability: Making AI outputs intuitive and accessible

11.4.3 Accountability and oversight: Managing responsibility in AI decisions

11.5 A proactive approach to AI governance

Summary