Ethics Guidelines for Trustworthy AI

By M. Cannarsa. 2021

Subfields: AI Ethics, Fairness & Society

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Citations in the corpus

Understanding artificial intelligence ethics and safety (2019, methodology): “3198732 European Commission. (2019). Ethics guidelines for trustworthy AI. Retrieved from https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-ai European Group on Ethics in Science and New Technologies. (2018). Artificial intelligence, robotics, and ‘autonomous’ systems.”
In AI We Trust? Factors That Influence Trustworthiness of AI-infused Decision-Making Processes (2019, background): “Governments may have their own requirements about what information must be disclosed about an AI model (see [17]).”
Reducing Gender Bias in Neural Machine Translation as a Domain Adaptation Problem (2020, neutral): “Recent recommendations for ethics in Artiﬁcial Intelligence have suggested that social biases or imbalances in a dataset be addressed prior to model training (HLEG, 2019).”
In AI We Trust: Ethics, Artificial Intelligence, and Reliability (2020, background): “They propose that there are three main characteristics of trusting AI: • The AI technology itself; • Designers and organisations behind the development, deployment and use of AI; and • The socio-technical systems involved in the AI life cycle (HLEG 2019, p. 5).”
Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey (2020, background): “Recent interest in XAI, even from governments especially with the European General Data Protection Regulation (GDPR) [21] regulation, shows the important realization of the ethics [22]–[26], trust [27]–[29], bias [30]–[33] of AI, as well as the”
Artificial Intelligence, Responsibility Attribution, and a Relational Justification of Explainability (2019, background): “This is why traceability (Dignum et al. 2018; Mittelstadt et al. 2016) is an important way to operationalize responsibility and explainability (European Commission AI HLEG 2019).”
Fairness And Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, And Mitigation Strategies (2023, background): “Overall, the study aims to contribute to ongoing efforts to develop more responsible and ethical AI systems by shedding light on the sources, impacts, and mitigation strategies of fairness and bias in AI.”
HARK Side of Deep Learning - From Grad Student Descent to Automated Machine Learning (2019, background): “As AI practitioners, we should ensure that humans are made aware of - or able to request and validate the fact that – they interact with an AI identity [61].”
Statistical stability indices for LIME: Obtaining reliable explanations for machine learning models (2020, background): “…a particularly delicate issue in CRM, since it is a highly regulated ﬁeld: GDPR (Kingston, 2017), as well as the ”Ethical Guidelines for trustworthy AI” (HLEG, 2019) and the Report from the ”European Banking Authority” (EBA, 2020) testify the care dedicated to such topics by the European Community.”