Algorithmic Fairness, Accountability and Ethics
This course provides an overview on the topics of bias and fairness in data, models, and algorithms, and on the associated ethical and accountability issues.
Whether to provide predictions, information, assessments, or evidence of some sort, the use of data-driven technologies comes with important responsibilities. In this course, you will learn to recognize and quantify how bias can affect data and algorithms, to apply methods for interpreting algorithmic predictions, to mitigate or manage the effects of bias and improve fairness in a number of applications, and understand why ethical issues can arise when applying data science to real world problems. Ultimately, the class will teach you how to reason through these problems in a systematic manner and how to justify and defend your approach to dealing with them.
The course will cover the following subjects:
- Bias Sources
- Fairness Metrics
- Explainability and Interpretability of Models
- Debiasing Data and Models
- Robustness of models
- Limits of Predictability
- Algorithms Auditing
- Old and new philosophical paradigms in AI
- General ethics - difference between fairness and justice
- Selected topics from philosophy of science and ethical systems for bias management
- The prerequisite required for admission to the course is knowledge of introductory machine learning.
- Students must be able to program. The default language is Python.
Intended learning outcomes
After the course, the student should be able to:
- Identify potential sources of bias and quantify fairness in data and applications
- Discuss strategies to mitigate or manage bias
- Analyse the robustness and caveats of machine learning models and predictions
- Apply tools to interpret/explain the predictions of a ML algorithm
- Reflect on the philosophical and ethical implications of data science applications to real world problems
Ordinary examExam type:
C: Submission of written work, External (7-point scale)
C1G: Submission of written work for groups