Advanced Certificate in Fairness and Accountability in AI
-- viewing now**Fairness** in AI is a pressing concern, and the Advanced Certificate in Fairness and Accountability in AI is designed to equip professionals with the knowledge to address it. Targeted at data scientists, engineers, and researchers, this program focuses on the principles and practices of fairness, accountability, and transparency in AI systems.
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Fairness, Accountability, and Transparency (FAT) in AI Systems: This unit introduces the concept of FAT in AI, its importance, and the challenges associated with achieving it. It covers the key principles of fairness, accountability, and transparency in AI decision-making. •
Bias in AI Systems: This unit explores the concept of bias in AI systems, its causes, and its consequences. It discusses the different types of bias, including dataset bias, algorithmic bias, and model bias, and provides strategies for mitigating bias in AI systems. •
Fairness Metrics and Evaluation: This unit introduces fairness metrics and evaluation methods for AI systems. It covers the different types of fairness metrics, including demographic parity, equalized odds, and calibration, and provides guidance on how to evaluate the fairness of AI systems. •
Fairness in Recruitment and Hiring: This unit applies the concepts of FAT to the context of recruitment and hiring. It discusses the challenges of achieving fairness in recruitment and hiring, and provides strategies for mitigating bias in these processes. •