Bias and Discrimination in AI

Description

In this course, you will learn:

• Understanding all dimensions of bias and discrimination
• In this paper, we look at the negative impacts of bias in machine learning (discriminatory effects of algorithmic decision-making)
• Identifying bias and discrimination causes in machine learning
• Machine learning bias mitigation (strategies for addressing bias)
• Recommendations for developing and evaluating algorithms in an ethical manner.

Syllabus:

1. The concepts of bias and fairness in AI
2. Fields where problems were diagnosed
3. Institutional attempts to mitigate bias and discrimination in AI
4. Technical attempts to mitigate bias and discrimination in AI