Algorithmic Fairness, Accountability and Ethics (Spring 2022)

Abstract

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.

Description

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

Formal prerequisites

• 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

Learning activities

The course consists of lectures and exercises. Beyond lectures and exercise sessions, we will have various online activities to be done before and after classes. These online activities will include: readings and videos, discussion on forums, writing documents, and coding exercises. During the class, we will have group discussions, class discussions, quizzes, writing sessions, and various hands-on exercises based on the activities done at home.

Mandatory activities

During the course, there will be three mandatory assignments, which focus on specific parts of the final exam project. These mandatory assignments are checkpoints on the status of the project and opportunities to receive feedback during the course. Students that miss the deadline for the mandatory activities during the course are required to submit the assignment together with the final exam project.

The student will receive the grade NA (not approved) at the ordinary exam, if the mandatory activities are not approved and the student will use an exam attempt.

Course literature

• Fairness and Machine Learning Book
  https://fairmlbook.org/

• Interpretable Machine Learning
  https://christophm.github.io/interpretable-ml-book/

• More literature is published in the course page in LearnIT.

Student Activity Budget

Estimated distribution of learning activities for the typical student

• Preparation for lectures and exercises: 15%
• Lectures: 25%
• Exercises: 20%
• Assignments: 10%
• Project work, supervision included: 30%

Ordinary exam

Exam type:
C: Submission of written work, Internal (7-point scale)
Exam variation:
C1G: Submission of written work for groups

reexam

Exam type:
C: Submission of written work, Internal (7-point scale)
Exam variation:
C1G: Submission of written work for groups

Time and date