Fairness, Accountability, Transparency, and Ethics in Data Processing (FATE)

The course focuses on three key issues in data processing, addressed from an ethical, legal, and technological perspective:

1. Personal data processing: privacy, confidentiality, surveillance, recourse, data collection, and power differentials

2. Data-driven decision support: biases and transparency in data processing, data-rich communication, and data visualization

3. Automated decision making: conceptualizations of power and discrimination in scenarios with different degrees of automation

We will spend about half of the course studying computing technologies for, e.g., anonymizing data, or detecting and mitigating algorithmic bias. The other half of the course we will study different conceptualizations of power around data processing pipelines, analyze bias and discrimination in computer systems from a moral philosophy perspective, and overview the relevant legal frameworks for data processing.

In the 2024 edition (January-March 2024), the course includes:

• 12 theory sessions in which the main concepts, background, methods, criteria will be introduced.

• 6 seminar sessions in which case studies of current and envisioned scenarios of data processing will be conducted  in small groups.

• 6 practice sessions in which data analysis tools will be used to detect and mitigate some of the issues discussed in the course.

Carlos Castillo — UPF, Department of Information and Communication Technologies (Coordinator)

Migle Laukyte — UPF, Faculty of Law

Antoni Rubí Puig — UPF, Faculty of Law

Gloria González Fuster — Vrije Universiteit Brussel, Faculty of Law and Criminology

Practices instructor:

Manuel Portela — UPF