Data Hazards: An open-source vocabulary of ethical hazards for data-intensive projects
Understanding the potential for downstream harms from data-intensive technologies requires strong collaboration across disciplines and with the public. Having shared vocabularies of concerns reduces the communication barriers inherent in this work. The Data Hazards project [url] contains an open-source, controlled vocabulary of 11 hazards associated with data science work, presented as ‘labels’. Each label has (i) an icon, (ii) a description, (iii) examples, and, crucially, (iv) suggested safety precautions. A reflective discussion format and resources have also been developed. These have been created over three years with feedback from interdisciplinary contributors, and their use evaluated by participants (N=47). The labels include concerns often out-of-scope for ethics committees, like environmental impact. The resources can be used as a structure for interdisciplinary harms discovery work, for communicating hazards, collecting public input or in educational settings. Future versions of the project will develop through feedback from open-source contributions, methodological research and outreach. • The Data Hazards are a vocabulary of 11 ethical hazards in data intensive work. • The project provides structure for discussion and reflection to prevent Data Hazards. • Safety precautions can be used to mitigate potential risks of each Data Hazard. • The project is hosted at datahazards.com and invites new contributions.
Authors
- Ismael Kherroubi Garcia (ORCID: https://orcid.org/0000-0002-6850-8375)
- Vanessa Aisyahsari Hanschke
- Philip Clatworthy (ORCID: https://orcid.org/0000-0002-1206-3573)
- Huw Day
- Euan D. Bennet (ORCID: https://orcid.org/0000-0001-9049-1920)
- Nina H. Di Cara (ORCID: https://orcid.org/0000-0002-6179-1067)
- E. S. Kuwertz
- Natalie Zelenka
- Susana Román García (ORCID: https://orcid.org/0009-0004-6967-7754)
Institutions
- North Bristol NHS Trust (GB)
- University of Bristol (GB)
- Tiri (United Kingdom) (GB)
- University College London (GB)
- University of Glasgow (GB)
- University of Edinburgh (GB)
Publication Details
- Journal
- Journal of Responsible Technology
- Published
- 2025-02-12
- DOI
- https://doi.org/10.1016/j.jrt.2025.100110
- Citations
- 1
- Primary Topic
- Big Data and Business Intelligence
- Type
- article
- Field-Weighted Citation Impact
- 2.28