Algorithmic Fairness and Accountability

About

This course examines automated decision-making systems and other algorithmic systems from a critical perspective, highlighting both their potential and the risks they pose to our societies. Students will explore how these systems can advance our societies but also reinforce historical mechanisms of discrimination. The course introduces theoretical frameworks, as well as computational and qualitative methods from both computer science and the social sciences, to critically audit the real-world impacts of algorithmic systems. It covers key topics including bias, interpretability, algorithmic epistemologies, and socio-technical imaginaries. Through key readings from major conferences and academic journals such as ACM Fairness, Accountability and Transparency (ACM FAccT) or the AAAI/ACM Conference on AI, Ethics, and Society, the course will also focus, from a more empirical perspective on surveillance technologies, large language models (LLMs), and AI’s environmental impacts, encouraging students to develop their critical thinking and reflect on the societal implications of datafication.

This is an interdisciplinary course and students from any background and discipline are welcome to join.

Topics

• Computer science: Concepts, definitions and metrics of bias and algorithmic fairness. Frameworks and limitations of algorithmic accountability. Methods on algorithmic interpretability. The feedback loop.
• Social science: Algorithmic governance, power, and the epistemology of algorithmic knowledge. Socio-technical imaginaries of algorithmic systems. Datafication genealogies. Studying up.
• Case studies: The welfare state, predictive policing and court systems. AI and Human Rights frameworks. The fairness of surveillance technologies. How to audit an LLM. How to investigate the environmental impact of AI supply chains.

Learning Outcomes

By the end of this course, you will have developed skills to:

• Understand how to conduct an algorithmic audit by implementing a range of methodologies and frameworks from multiple disciplines. You will learn how concepts like algorithmic fairness and accountability are defined and operationalised in legal contexts.
• Critically analyse how foundational aspects of decision-making systems are connected to historical mechanisms of discrimination. You will also be able to identify the benefits, risks, and limitations of algorithmic systems, and recognize key publications in the field of critical data studies, featuring authors from both the Global North and South.
• Become familiar with emerging theories, concepts, and empirical methods in the area of algorithmic fairness. For instance, you will learn how to investigate the environmental impacts of AI supply chains.

By the end of this course, you will have developed skills to:

• Understand how to conduct an algorithmic audit by implementing a range of methodologies and frameworks from multiple disciplines. You will learn how concepts like algorithmic fairness and accountability are defined and operationalised in legal contexts.
• Critically analyse how foundational aspects of decision-making systems are connected to historical mechanisms of discrimination. You will also be able to identify the benefits, risks, and limitations of algorithmic systems, and recognize key publications in the field of critical data studies, featuring authors from both the Global North and South.
• Become familiar with emerging theories, concepts, and empirical methods in algorithmic fairness and critical data studies. For instance, you will learn how to investigate the environmental impacts of AI supply chains.