Winter Data Wonder #2: Insights into the ethical, legal, and social dimensions of data science

Karlsruhe, 21 January 2026 — What specific ethical and legal issues arise in data science projects? And how do people deal with them in practice?

This is precisely what a recent study by Maria Christoforaki and Stephanie von Maltzan examines. Stephanie v. Maltzan is a research associate at FIZ Karlsruhe, where she works in the field of intellectual property rights, giving the study a clear legal perspective.

The study was conducted as part of NFDI4DataScience and is based on 30 anonymized expert interviews conducted between November 2022 and February 2024. The respondents come from both academia and industry and have very different roles, levels of experience, and professional backgrounds.

The focus is not on abstract principles, but on real experiences from specific projects. Topics include data protection, bias and discrimination, transparency and explainability, ELSA awareness, and collaboration between technical, legal, and specialist disciplines.

The aim of the study is clear: to highlight recurring ethical and legal challenges in data science workflows and understand how researchers and practitioners deal with them. Based on this, practical guidelines for responsible and trustworthy data science are to be developed.

The dataset is openly available under CC BY-NC-ND 4.0 and is suitable for analysis, teaching, and addressing real challenges from data science practice. A resource  very much worth reading for anyone interested in how data science projects actually deal with ethical, legal, and social challenges.

The dataset is available on Zenodo at https://lnkd.in/d5SZ6CTN