Establishing the rules for building trustworthy AI
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AI presents a range of potential future concerns for qualitative research due to its rapid technological advancement, novelty, and social context.
“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
The European Commission has provided seven different criteria on the basis of which trustworthiness can be proven.
“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]
The technical robustness and safety requirement includes functionalities aligned with preventing unintentional harm and minimizing consequences of intentional harm, including resilience to attacks and security, fallback plans, general safety, and being accurate, reliable and reproducible.
“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]
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.
“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]
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.
“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
Cites
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Self-citationSoft ethics, the governance of the digital and the General Data Protection Regulation
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Self-citationSome Moral and Technical Consequences of Automation—A Refutation
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“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.”
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“Technology, especially arti fi cial intelligence (AI), is a domain profoundly impacted by these challenges, as the accelerating pace of technological developments constantly introduces new ethical dilemmas (Floridi, 2019).”
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Park, W. Y., Jeon, K., Schmidt, T. S., Kondylakis, H., Alkasab, T. K., Dewey, B. E., You, S. C., & Nagy, P. G. (2024) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-024-00982-6 · Cited by 26 · see citing works
Towards Warranted Trust: A Model on the Relation Between Actual and Perceived System Trustworthiness
Schlicker, N., & Langer, M. (2021) · Mensch und Computer (MuC) · doi:10.1145/3473856.3474018 · Cited by 26 · see citing works
Ethics dumping in artificial intelligence
Bélisle-Pipon, J.-C., & Victor, G. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1426761 · Cited by 26 · see citing works
Pezoulas, V. C., Goules, A. V., Kalatzis, F., Chatzis, L. G., Kourou, K. D., Venetsanopoulou, A. I., Exarchos, T. P., Gandolfo, S., Votis, K., Zampeli, E., et al. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.01.002 · Cited by 25 · see citing works
Backdoor Attacks in Federated Learning by Rare Embeddings and Gradient Ensembling
Yoo, K., & Kwak, N. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.6 · Cited by 26 · see citing works
A principle‐based approach to AI: The case for European Union and Italy
Corea, F., Fossa, F., Loreggia, A., Quintarelli, S., & Sapienza, S. (2023) · AI & SOCIETY · doi:10.1007/s00146-022-01453-8 · Cited by 25 · see citing works
Explainable artificial intelligence in ophthalmology
Tan, T. F., Dai, P., Zhang, X., Jin, L., Poh, S. S. J., Hong, D. C., Lim, J., Lim, G., Teo, Z. L., Liu, N., et al. (2023) · Current Opinion in Ophthalmology · doi:10.1097/icu.0000000000000983 · Cited by 27 · see citing works
A Predictive Analytics Infrastructure to Support a Trustworthy Early Warning System
Bañeres, D., Guerrero‐Roldán, A., Rodríguez, M. E., & Karadeniz, A. (2021) · Applied Sciences · doi:10.3390/app11135781 · Cited by 28 · see citing works
- 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
- Social Sciences › Safety Research › 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
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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.
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