ArticlePhilosophical transactions. Series A, Mathematical, physical, and engineering sciences2016
Locating ethics in data science: responsibility and accountability in global and distributed knowledge production systems.
Article in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed.
- Improving FAIRness of Geospatial data using Large Language Models.Scientific data · 2026Article
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- An ecosystem approach to governing commercial actors in healthcare AI.Policy studies (Policy Studies Institute) · 2025Article
- Data Hazards as An Ethical Toolkit for Neuroscience.Neuroethics · 2025Article
- Decolonisation and Self-Regulation as Alternative Paths to Data Science Health Research Governance in Africa.Wellcome open research · 2025Article
- The promise of data science for health research in Africa.Nature communications · 2023Review
- Epistemic Rights and Responsibilities of Digital Simulacra for Biomedicine.The American journal of bioethics : AJOB · 2023Article
- Patient and public involvement to build trust in artificial intelligence: A framework, tools, and case studies.Patterns (New York, N.Y.) · 2022Review
- Teaching Responsible Data Science: Charting New Pedagogical Territory.International journal of artificial intelligence in education · 2022Article
- Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed.Human brain mapping · 2022Review
- The case for open science: rare diseases.JAMIA open · 2020Review
- Metadata accounts: Achieving data and evidence in scientific research.Social studies of science · 2019Article
- Authorship: from credit to accountability. Reflections from the Editors' Network.Clinical research in cardiology : official journal of the German Cardiac Society · 2019Review
- Scientific Data Management in the Age of Big Data: An Approach Supporting a Resilience Index Development Effort.Frontiers in environmental science · 2019Article
- Authorship: from credit to accountability : Reflections from the Editors' Network.Netherlands heart journal : monthly journal of the Netherlands Society of Cardiology and the Netherlands Heart Foundation · 2019Article
- Authorship: From Credit to Accountability Reflections From the Editors´ Network.Anatolian journal of cardiology · 2019Article
- Concealment and discovery: The role of information security in biomedical data re-use.Social studies of science · 2018Article
- What is data ethics?Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2016Article
- Preventing Diffusion of Moral Responsibility in Big Team Science: Taking an Ecosystem Approach.The Hastings Center reportArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
The distributed and global nature of data science creates challenges for evaluating the quality, import and potential impact of the data and knowledge claims being produced. This has significant consequences for the management and oversight of responsibilities and accountabilities in data science. In particular, it makes it difficult to determine who is responsible for what output, and how such responsibilities relate to each other; what 'participation' means and which accountabilities it involves, with regard to data ownership, donation and sharing as well as data analysis, re-use and authorship; and whether the trust placed on automated tools for data mining and interpretation is warranted (especially as data processing strategies and tools are often developed separately from the situations of data use where ethical concerns typically emerge). To address these challenges, this paper advocates a participative, reflexive management of data practices. Regulatory structures should encourage data scientists to examine the historical lineages and ethical implications of their work at regular intervals. They should also foster awareness of the multitude of skills and perspectives involved in data science, highlighting how each perspective is partial and in need of confrontation with others. This approach has the potential to improve not only the ethical oversight for data science initiatives, but also the quality and reliability of research outputs.This article is part of the themed issue 'The ethical impact of data science'.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.