What Is Data Ethics?
Open accessAbstract
This theme issue has the founding ambition of landscaping data ethics as a new branch of ethics that studies and evaluates moral problems related to data (including generation, recording, curation, processing, dissemination, sharing and use), algorithms (including artificial intelligence, artificial agents, machine learning and robots) and corresponding practices (including responsible innovation, programming, hacking and professional codes), in order to formulate and support morally good solutions (e.g. right conducts or right values). Data ethics builds on the foundation provided by computer and information ethics but, at the same time, it refines the approach endorsed so far in this research field, by shifting the level of abstraction of ethical enquiries, from being information-centric to being data-centric. This shift brings into focus the different moral dimensions of all kinds of data, even data that never translate directly into information but can be used to support actions or generate behaviours, for example. It highlights the need for ethical analyses to concentrate on the content and nature of computational operations—the interactions among hardware, software and data—rather than on the variety of digital technologies that enable them. And it emphasizes the complexity of the ethical challenges posed by data science. Because of such complexity, data ethics should be developed from the start as a macroethics, that is, as an overall framework that avoids narrow, ad hoc approaches and addresses the ethical impact and implications of data science and its applications within a consistent, holistic and inclusive framework. Only as a macroethics will data ethics provide solutions that can maximize the value of data science for our societies, for all of us and for our environments. This article is part of the themed issue ‘The ethical impact of data science’.
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In the digital age, human and machine agents are so closely intertwined in hybrid collectives that their relations determine their actions.
“In order to fully understand the automation-augmentation paradox's societal implications, scholars could adopt a relational ontology which accepts that, in the digital age, human and machine agents are so closely intertwined in hybrid collectives that their relations determine their actions (Floridi & Taddeo, 2016).”
From Artificial Intelligence and Management: The Automation–Augmentation Paradox (2021) · cites this paper as Floridi & Taddeo, 2016
The Introduction says that the ethics of data, algorithms and practices are intertwined, and the Abstract discusses interactions among hardware, software and data. Neither passage addresses a relational ontology in which human and machine agents form hybrid collectives or their relations determine their actions. Searches for “hybrid collectives” and “relations determine their actions” found no supporting passage in our copy; page numbers were not available. (LLM, conf=0.75)
Experimental — accuracy not yet measuredThe application of AI in healthcare raises significant concerns of fairness, responsibility, and human rights.
“The application of AI in healthcare therefore raises significant concerns of fairness, responsibility, human rights (Floridi & Taddeo, 2016)”
From Responsible AI for Digital Health: a Synthesis and a Research Agenda (2023) · cites this paper as Floridi & Taddeo, 2016
Deploying AI is coupled with significant ethical challenges.
“Recent empirical articles highlight how deploying AI is coupled with significant ethical challenges (Floridi & Taddeo, 2016)”
From Responsible AI for Digital Health: a Synthesis and a Research Agenda (2023) · cites this paper as Floridi & Taddeo, 2016
AI technologies pose unique ethical risks to fundamental rights that deserve special attention.
“At the same time, these novel features mean that AI technologies pose unique ethical risks to fundamental rights that deserve special attention (Floridi and Taddeo 2016;Tsamados et al. 2021).”
From Artificial intelligence in support of the circular economy: ethical considerations and a path forward (2024) · cites this paper as Floridi and Taddeo 2016
Data in itself can be a source of manifold ethical problems.
“At the same time, the nascent stream of data ethics tells us that data in itself can be a source of manifold ethical problems (Floridi and Taddeo 2016).”
From Trustworthy artificial intelligence (2021) · cites this paper as Floridi and Taddeo 2016
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- Authors
- Floridi, L., & Taddeo, M.
- Affiliations
- University of Oxford (GB)
- Corresponding author
- Floridi, Luciano
- Venue
- Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
- Publisher
- The Royal Society
- Published
- 2016-12-28
- Volume
- 374
- Issue
- 2083
- Pages
- 20160360
- ISSN
- 1364-503X (ISSN-L)
- 1471-2962
- PMC
- PMC5124072
- OpenAlex
- W2554467860
- Cited by
- 637
- References
- 24
- Citations by year
- 2026: 66 · 2025: 159 · 2024: 107 · 2023: 51 · 2022: 57 · 2021: 59 · 2020: 49 · 2019: 48 · 2018: 28 · 2017: 10 · 2016: 3
- FWCI
- 72.38
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- SSRN Electronic Journal (submittedVersion)
- PhilPapers (PhilPapers Foundation) (submittedVersion)
- Funders
- Alan Turing Institute (TU/B/000055, TU/B/000057)
- Strong
- Engineering and Physical Sciences Research Council (EP/N02334X/1, EP/N023013/1)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Sociology and Political Science › Flooding and Environmental Impact
- Social Sciences › Law › Digitalization, Law, and Regulation
- Keywords
- data ethics, macroethics, data science ethics, information ethics, algorithms, artificial intelligence, responsible innovation
- MeSH terms
- Information Science
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