Establishing the rules for building trustworthy AI
By Luciano Floridi. Nature Machine Intelligence, 2019
376 citations recorded by the source corpus.
Subfields: Cognitive Neuroscience, Health Informatics, Safety Research
View Paper: https://www.semanticscholar.org/paper/dc44e2be0f85b6225f05390c570885337a99ef83
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cognitive Neuroscience
- Jessica Morley
- Emilio Ferrara
- Anat Elhalal
- Libby Kinsey
- Mark Coeckelbergh
- Ali Sunyaev
- Sebastian Lins
- Scott Thiebes
In Health Informatics
- Emilio Ferrara
- Jessica Morley
- Anat Elhalal
- Libby Kinsey
- Ala Al-Fuqaha
- Ali Sunyaev
- Francisco Herrera
- Junaid Qadir
In Safety Research
- Jessica Morley
- Anat Elhalal
- Libby Kinsey
- Emilio Ferrara
- Mark Coeckelbergh
- Ali Sunyaev
- Sebastian Lins
- Scott Thiebes
Citations in the corpus
- Algorithm fairness in artificial intelligence for medicine and healthcare (2023, background): “There are many different types of contract (concerning technical robustness, safety, non-discrimination and transparency, in particular) outlined by the European Commission’s ethical guidelines for trustworthy AI 306,307 .”
- Trustworthy AI: From Principles to Practices (2021, background): “• We consider ethical requirements with broad concerns in recent literature [9, 57, 121, 145, 178, 199, 218, 301, 334], including fairness, privacy, and accountability.”
- From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices (2019, methodology): “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.”
- Trustworthy artificial intelligence (2020, background): “, general ethics frameworks) (Floridi 2019; Floridi and Cowls 2019; Floridi et al. 2018; Hagendorff 2020) and how to embed values that Trustworthy artificial intelligence”
- Explainable, trustworthy, and ethical machine learning for healthcare: A survey (2021, background): “In particular, an explanation should contain the complete information and not misguide the end-user [28].”
- Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Models (2023, background): “AI ethics guidelines and fairness frameworks aim to provide guidance and best practices for developers, organizations, and policy-makers to reduce bias and ensure responsible development and deployment of AI systems [128, 129].”
- Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation (2023, background): “Citizens should have full control over their data, and their data will not be unlawfully or unfairly used to harm or discriminate against them [28].”