Trustworthy AI: Managing Bias, Ethics, and Accountability

What the course examines

This course looks at AI and machine learning from three angles: the ethical, the social and the technical. It starts from the observation that bias exists in people as well as in machines, then moves to the strategies organisations use to reduce the risks that follow from it.

Why the regulatory angle matters

Fairness and accountability are hard to pin down on their own. The regulatory landscape gives them shape, because it spells out who must answer for an automated decision and what evidence they need to keep. Learners in compliance, product or data roles will find that link especially useful.

Who will benefit

Pitched at an intermediate level, the course suits people who already know basic machine learning terms and want to think about how those systems affect real people. Case studies and real-world examples test the ideas instead of leaving them abstract.

Takeaways

• Bias is a property of both human decisions and the systems built from them.
• Mitigation starts with naming the specific risk in a specific context.
• Accountability is a design question, not only a legal one.