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Trustworthy AI—Part 1
Open accessAbstract
To improve the trustworthiness of artificial intelligence systems, organizations should address the risks relevant to the use case whilst taking into account the stakeholders that interact directly or indirectly with the system.
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Robustness and reliability refer to the ability of the system to have comparable performance on atypical data with respect to typical operational regimes.
“When dealing with an AI-based system, robustness and reliability are properties that refer to the ability of the system to have comparable performance on atypical data with respect to typical operational regimes [29].”
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 [29]
IEEE, ISO/IEC and CEN/CENELEC are implementing concrete guidelines to apply trustworthy AI requirements in industrial setups.
“This includes IEEE, ISO/IEC and CEN/CENELEC, which are implementing concrete guidelines to apply trustworthy AI requirements in industrial setups (see [11,29] for an overview).”
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 [29]
Assuring properties of non-discrimination, fairness and bias mitigation restricts systematic differences treating certain groups with respect to others.
“Assuring properties of non-discrimination, fairness and bias mitigation restrict the systematic differences treating certain groups (of people or objects) with respect to others [29].”
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 [29]
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Research works printed but not linked: 14
- 2022, Information Technology Artificial Intelligence Artificial Intelligence Concepts and Terminology
- 2020, Information Technology Artificial Intelligence Overview of Trustworthiness in Artificial Intelligence
- 2020, German standardization roadmap for AI
- 2017, Ethically designed AI version 2
- 2020, Empowering AI leadership - An oversight toolkit for boards of directors
- 2021, Building trust in AI and ML principles
- 2021, Trustworthy AI implementation framework for AI systems
- 2019, Ethical guidelines for trustworthy AI: High level expert group on AI
- 2021, IEEE Standard Model Process for Addressing Ethical Concerns during System Design
- 2022, capAI - A procedure for conducting conformity assessment of AI systems in line with the EU artificial intelligence act
- 2021, NOREA guiding principles trustworthy AI investigations
- Zicari, 2022, How to assess trustworthy AI in practice
- 2021, Tools for trustworthy AI
- Artificial Intelligence Functional Safety and AI Systems
Cited by
Sorted by citations
Díaz-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
Metadata
- Authors
- Mariani, R., Rossi, F., Cucchiara, R., Pavone, M., Simkin, B., Koene, A., & Papenbrock, J.
- Affiliations
- NVIDIA (Italy) (IT)
- IBM (United States) (US)
- University of Modena and Reggio Emilia (IT)
- Stanford University (US)
- Nvidia, Berlin, Germany
- Global Artificial Intelligence (AI) Ethics and Regulatory Leader, Ernst & Young, Brussels, Belgium
- Financial Technology in the Europe, Middle East, and Africa (EMEA) region, Nvidia, Frankfurt, Germany
- ORCID
- Venue
- Computer
- Published
- 2023-02
- Volume
- 56
- Issue
- 2
- Pages
- 14-18
- ISSN
- 0018-9162 (ISSN-L)
- 1558-0814
- OpenAlex
- W4319879433
- Cited by
- 7
- References
- 0
- Citations by year
- 2025: 3 · 2024: 1 · 2023: 3
- FWCI
- 1.75
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Computer (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Safety Systems Engineering in Autonomy
- Keywords
- trustworthy AI, AI risk management, stakeholder engagement, AI use cases, AI governance
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