Turing Institute

The ethics of algorithms: Mapping the debate

Brent Mittelstadt, Patrick Allo, Mariarosaria Taddeo et al.

2016 · 2.635 Zit.

The ethics of AI in health care: A mapping review

Jessica Morley, Caio C. Vieira Machado, Christopher Burr et al.

2020 · 805 Zit.

Artificial Intelligence and the ‘Good Society’: the US, EU, and UK approach

Corinne Cath, Sandra Wachter, Brent Mittelstadt et al.

2017 · 657 Zit.

Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness

Sebastian J. Vollmer, Bilal A. Mateen, Gergő Bohner et al.

2020 · 460 Zit.

A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME

Ahmed Salih, Zahra Raisi‐Estabragh, Ilaria Boscolo Galazzo et al.

2024 · 458 Zit.

Transparent, explainable, and accountable AI for robotics

Sandra Wachter, Brent Mittelstadt, Luciano Floridi

2017 · 391 Zit.

The ethics of algorithms: key problems and solutions

Andreas Tsamados, Nikita Aggarwal, Josh Cowls et al.

2021 · 306 Zit.

Consistency of variety of machine learning and statistical models in predicting clinical risks of individual patients: longitudinal cohort study using cardiovascular disease as exemplar

Li Yan, Matthew Sperrin, Darren M. Ashcroft et al.

2020 · 139 Zit.

Considering Biased Data as Informative Artifacts in AI-Assisted Health Care

Kadija Ferryman, Maxine Mackintosh, Marzyeh Ghassemi

2023 · 94 Zit.

Holding AI to Account: Challenges for the Delivery of Trustworthy AI in Healthcare

Rob Procter, Peter Tolmie, Mark Rouncefield

2022 · 51 Zit.

Multi Scale Ethics—Why We Need  to Consider the Ethics of AI in Healthcare at Different Scales

Melanie Smallman

2022 · 34 Zit.

Towards a Framework for Evaluating the Safety, Acceptability and Efficacy of Ai Systems for Health: An Initial Synthesis

Jessica Morley, Caroline E Morton, Kassandra Karpathakis et al.

2021 · 32 Zit.

Development and validation of open-source deep neural networks for comprehensive chest x-ray reading: a retrospective, multicentre study

Yashin Dicente Cid, Matthew Macpherson, Louise Gervais-Andre et al.

2023 · 26 Zit.

Can surgeons trust AI? Perspectives on machine learning in surgery and the importance of eXplainable Artificial Intelligence (XAI)

Johanna M. Brandenburg, Beat P. Müller‐Stich, Martin Wagner et al.

2025 · 22 Zit.

Google Health and the NHS: overcoming the trust deficit

Jessica Morley, Mariarosaria Taddeo, Luciano Floridi

2019 · 18 Zit.