The Alan Turing Institute | Bias in AI – Beyond Binary Classification | 25 hours

From the Bias in AI – Beyond Binary Classification page;

Course overview

Artificial Intelligence is widely used in sensitive domains such as healthcare, insurance, recruitment and credit scoring. In these cases, it is imperative that algorithms work fairly for all target users, without discriminating certain groups over others. The responsible AI community has been active in creating tools and techniques for measuring and mitigating bias. However, most of the literature has so far been concerned with binary classification tasks. This course will extend beyond binary classification. We will cover how to measure and mitigate bias in a variety of tasks such as multiclass classification, regression, recommender systems and clustering algorithms.

Duration

25-30 hours

Who is this course for?

The course is designed for a technical audience, specifically data science and machine learning practitioners or researchers who are concerned about the fairness of their algorithms. 

You are expected to know:

• basic linear algebra
• machine learning and 
• programming (the programming language used for exercises is Python)

Additionally, the course is built as an extension of a previous course on Assessing and Mitigating Bias and Discrimination in AI. You should therefore be familiar with basic concepts of fairness, and how to measure and mitigate bias in binary classification tasks.

Learning outcomes

By the end of the course, you will be able to:

• explain why fairness is an issue in regression, multiclass classification, recommender systems and clustering tasks
• describe different definitions of fairness that apply to these tasks
• measure and mitigate fairness for regression, multiclass classification, recommender systems and clustering tasks
• practice on a range of case studies and practical programming exercises
• explain how robustness, privacy and explainability interact with fairness in regression, multiclass classification, recommender systems and clustering tasks