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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 (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
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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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“• We consider ethical requirements with broad concerns in recent literature [9, 57, 121, 145, 178, 199, 218, 301, 334], including fairness, privacy, and accountability.”
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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
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