Ethics, Fairness and Explanation in AI - COMP70076
As AI becomes more developed and successful, and is adopted more widely in industry and across society, the ethical and social issues raised by AI become more pressing. Practitioners of AI should be aware of these issues, and this module is intended to achieve that. The module divides into three parts. One, on the ethics of AI, is about ethical and philosophical problems raised by AI—such as the alignment problem, and the attribution of responsibility for autonomous agents. The second, on fairness and bias in machine learning (ML), concerns conceptions of algorithmic fairness and bias, the accuracy/bias trade-off, and practical approaches in these areas. The third, on explainable AI (XAI), will concern approaches for understanding why a given AI has reached specific decisions—explanations for decisions being essential for understanding whether those decisions are justified, and thus ethical.