Ethical and Legal Guidelines for Data Scientists

Data Science (DS) and Artificial Intelligence (AI) are transforming research, industry and society at an unprecedented pace, enabling advances in areas such as healthcare, finance, e-commerce and beyond. Despite their potential, the rapid development and widespread use of DS and AI raise (novel) issues of reliability, accuracy, copyright and data protection, and bias and discrimination, among others. It is therefore vital for data scientists to acknowledge and cope with the Ethical, Legal and Societal Aspects (ELSA) encountered in DS and AI projects. Within the NFDI4DataScience framework, specifically in the Community and Training task area, one way to address this challenge is to develop ELSA guidelines for data scientists (other means include creating educational material, within the same task area). To achieve this objective, we assessed the landscape by interviewing researchers and practitioners in the field to identify and analyse the most common ELSA challenges encountered in DS/AI projects and how to cope with them. In this document, we present the guidelines which were developed as a result of the above-described process. The basic data points we considered for the development of the guidelines are the following: ● Data Scientists should be able to recognise the ethical, legal, and societal aspects of their work. The interviews’ analysis shows that data scientists are generally aware of ELSA issues; however, nuances often escape them; ● The guidelines are not meant to substitute ELSA experts or turn data scientists into them; they are merely a way to develop a common language with the relevant domain experts in order to cooperate to find appropriate solutions; ● We tried to incorporate ELSA into the data science workflow, making it easier for Data Scientists to follow the guidelines in their day-to-day work and not see it as an impediment or a superfluous artefact. The Guidelines are complemented by a Checklist that helps the Data Scientist review their project via a series of questions corresponding to the before/during/after project phases. This checklist is only for practical self-assessment; it does not determine lawfulness by itself and does not replace a DPIA, FRIA, DPO, legal department, or ethics committee, so one should not proceed on the basis of this checklist alone. We also provide a (not exhaustive) list of educational materials for further study, linked to the respective guideline subjects. The educational material comprises a variety of sources ranging from scientific papers to links to legal texts (all links were active when accessed at the time of publication of this document in September 2026. Since this document is not a living one, readers are advised to search for the most up-to-date versions, especially regarding legal texts). [1] The anonymised interviews, the accompanying material, and a study guide can be found at: Christoforaki, Maria, and Stephanie von Maltzan. ‘Ethical, Legal, and Societal Aspects of Data Science as Manifested via a Series of Interviews Conducted within the Framework of NFDI4DataScience, Study Guide and Anonymised Transcripts’. Zenodo, 17 November 2025. https://doi.org/10.5281/zenodo.17629214.