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AI Governance Explained: Oversight for Intelligent Systems
A plain-language introduction to AI governance: how organizations and governments set rules, roles and checks for building and using AI systems responsibly.
Policy and Governance
AI Governance and Risk
Oversight, bias, transparency and responsible use of AI.
AI and Privacy: Where Personal Data Enters the Picture
How AI systems can use, infer or expose personal data at each stage, from training to everyday use, and what organizations and individuals often weigh.
AI Risk Assessment: Questions Organizations Ask Before Deploying
A general approach to AI risk assessment: the questions about purpose, data, impact, oversight and monitoring that organizations often work through before launch.
Algorithmic Bias: How Unfair Outcomes Can Arise
Where algorithmic bias comes from, including skewed data, design choices and feedback loops, and how researchers and organizations examine systems for unfair outcomes.
Automated Hiring Tools: Promises and Concerns
What automated hiring tools claim to do, what critics worry about, and the questions employers and candidates often raise about algorithmic screening.
Copyright and Generative AI: The Open Questions
An overview of unresolved debates about AI copyright: training on existing works, ownership of AI outputs, similarity to originals, and how courts and lawmakers differ.
Explainable AI: Why Transparency Matters
What explainable AI tries to do, why understanding automated decisions matters, the main techniques used, and the limits of explanations for complex models.
Human Oversight of Automated Decisions
How people can stay involved in decisions made with AI: the main oversight models, what makes review meaningful, and common pitfalls like automation bias.
Responsible AI Principles Compared
A survey of the themes that recur in published responsible AI frameworks, such as fairness, transparency, accountability, safety and privacy, and where they pull apart.
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