MSc in Fundamental Principles of Data Science | Universitat de Barcelona
- Web Site in Campus Virtual UB: Ethical Data Science
Data science has the potential to be both beneficial and detrimental to individuals and/or the wider public. To help minimize any adverse effects, we must seek to understand the potential impact of our work and consider any opportunities that may deliver benefits for the public.
In this course, we will explore the social and ethical ramifications of the choices we make at the different stages of the data analysis pipeline, from data collection and storage to understand feedback loops in the analysis. Through case studies, and exercises, students will learn the basics of ethical thinking, understand some tools to check or mitigate undesired effects, and study the distinct challenges associated with ethics in modern data science.
- Jordi Vitrià, Departament de Matemàtiques i Informàtica de la UB.
- Itziar de Lecuona, Bioethics and Law Observatory at the University of Barcelona.
- Second Semester (February, 2021 - May, 2021)
- Face-to-face Lectures: Wednesday 15:00h-16:00h
- Location: Online during COVID-19 pandemic
- Proficiency in Python (3.7).
- Calculus, Linear Algebra: You should be comfortable taking derivatives and understanding matrix vector operations and notation.
- Basic Probability and Statistics.
There will be three 1500-word essays due during the course on assigned topics and one practical exercise.
Writing assignments evaluation will emphasize correctness in your writing and a good understanding of the course material (as well as rigor, precision, and clarity).
- Writting assignments must be formatted in the Tufte Essay format: Tufte Essay
- How to write an ethics paper in 5 easy steps.
- Ethical Foundations | Slides
- Podcast: Ethical problems with recommender systems by Silvia Milano
- Podcast: On morality and rationality by Sean Carroll
- Bias and Fairness | Slides | Slides II
- Video: Why Fairness Cannot Be Automated: Bridging the Gap Between EU Non-Discrimination Law and AI by S.Wachter.
- Podcast: Responsible AI at Facebook by Silvia Milano
- Course: A brief introduction to Causal Inference
- Video: Models for Algorithmic Fairness by R.Silva.
- Transparency and Explainability | Slides
- Book: Interpretable Machine Learning by C.Molnar.
- Video: Richard Feynman about “explanations” and “why questions”.
- Privacy and Data Agency | Slides
- Reading:
- Julie E. Cohen, What is privacy for, Harvard Law Review, Vol. 126, 2013.
- C.Véliz, Privacy Is Power: Why and How You Should Take Back Control of Your Data, Transworld Ed., 2020.
- E. Decristofaro. An Overview of Privacy in Machine Learning. https://arxiv.org/abs/2005.08679
- Video: Carissa Véliz on "Why privacy matters more than ever". Video
- Reading:
- Information Disorder | Slides
- Reading
- How Facebook got addicted to spreading misinformation, by K.Hao.
- Far-right news sources on Facebook more engaging, by Laura Edelson, Minh-Kha Nguyen, Ian Goldstein, Oana Goga, Tobias Lauinger, and Damon McCoy.
- Reading
- AI and the Alignment Problem | Slides