The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Establishing the rules for building trustworthy AI
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“AI presents a range of potential future concerns for qualitative research due to its rapid technological advancement, novelty, and social context (Floridi, 2019; McCradden et al., 2020).”
From Quality Criteria: General and Specific Guidelines for Qualitative Approaches in Psychology Research. A Concise Guide for Novice Researchers and Reviewers (2024) · cites this paper as Floridi, 2019
In the citing paper
“The EC has provided seven different criteria on the basis of which trustworthiness can be proven [13], [14] and these rules were formulated on the basis of three main criteria which are to ensure the transparency, reliability and for the protection of data in models [14].”
From A Review of Trustworthy and Explainable Artificial Intelligence (XAI) (2023) · cites this paper as [13]
In the citing paper
“This second requirement includes several functionalities all aligned with the prevention of unintentional harm and the minimization of the consequences of intentional harm . These include the resilience of AI-based systems (to attacks and security), ensuring fallback plans (in case something goes wrong), general safety, and being accurate, reliable and reproducible.”
From Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation (2023) · cites this paper as [28]
In the citing paper
“Robustness and safety refer to the need of AI systems to be secure, reliable and robust enough to errors and/or inconsistencies in all phases of the life cycle [28].”
From Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation (2023) · cites this paper as [28]
In the citing paper
“However, it is becoming increasingly evident that AI is not the 'magic bullet' some would like to believe it is and that AI, just like any other technology, will not only bring forth many benefits but will also be accompanied with a variety of novel ethical, legal, and social challenges (Floridi 2019;Floridi et al. 2018).”
From Trustworthy artificial intelligence (2021) · cites this paper as Floridi 2019
Citations
Cites
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., et al. (2018) · Minds and Machines · doi:10.1007/s11023-018-9482-5 · Cited by 4,774
Self-citationSoft ethics, the governance of the digital and the General Data Protection Regulation
Floridi, L. (2018) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2018.0081 · Cited by 234
Self-citationSome Moral and Technical Consequences of Automation—A Refutation
Samuel, A. L. (1960) · Science · doi:10.1126/science.132.3429.741 · Cited by 98
Glaspell, S. (1922) · Cited by 64
Cited by
Sorted by citations
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., et al. (2023) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2023.102642 · Cited by 4,584
Morley, J., Floridi, L., Kinsey, L., & Elhalal, A. (2020) · Science and Engineering Ethics · doi:10.1007/s11948-019-00165-5 · Cited by 882
“It is, therefore, a necessary building block in the creation of an environment that fosters ethical, responsible, and beneficial ML, especially as it also indicates the possibility of a time when the distractive risk of ethics shopping 13 (Floridi 2019c) will be lessened.”
BackgroundMethodInfluentialSelf-citationDíaz-Rodríguez, N., Del Ser, J., Coeckelbergh, M., López de Prado, M., Herrera-Viedma, E., & Herrera, F. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101896 · Cited by 800
Algorithmic fairness in artificial intelligence for medicine and healthcare
Chen, R. J., Wang, J. J., Williamson, D. F. K., Chen, T. Y., Lipkova, J., Lu, M. Y., Sahai, S., & Mahmood, F. (2023) · Nature Biomedical Engineering · doi:10.1038/s41551-023-01056-8 · Cited by 750
Jusup, M., Holme, P., Kanazawa, K., Takayasu, M., Romić, I., Wang, Z., Geček, S., Lipić, T., Boris Podobnik, A., Wang, L., et al. (2022) · Physics Reports · doi:10.1016/j.physrep.2021.10.005 · Cited by 688
Metadata
- Authors
- Floridi, L.
- Affiliations
- University of Oxford (GB)
- Corresponding author
- Floridi, Luciano
- ORCID
- Floridi, Luciano
- Venue
- Nature Machine Intelligence
- Published
- 2019-05-07
- Volume
- 1
- Issue
- 6
- Pages
- 261-262
- ISSN
- 2522-5839 (ISSN-L)
- OpenAlex
- W2944176689
- Cited by
- 394
- References
- 5
- Citations by year
- 2026: 57 · 2025: 70 · 2024: 52 · 2023: 102 · 2022: 51 · 2021: 41 · 2020: 13 · 2019: 8
- FWCI
- 49.42
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nature Machine Intelligence (publishedVersion)
- PhilPapers (PhilPapers Foundation) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Law › Digitalization, Law, and Regulation
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- trustworthy AI, AI ethics guidelines, European Commission, responsible AI development, AI regulation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
