Results 11 to 20 of about 97,157 (255)

Trustworthy human-AI partnerships [PDF]

iScience, 2021
Summary: In this paper, we foreground some of the key research challenges that arise in the design of trustworthy human-AI partnerships. In particular, we focus on the challenges in designing human-AI partnerships that need to be addressed to help humans
Sarvapali D. Ramchurn, Sebastian Stein, Nicholas R. Jennings   +2 more
doaj   +5 more sources

Trustworthy AI [PDF]

Proceedings of the 3rd ACM India Joint International Conference on Data Science & Management of Data (8th ACM IKDD CODS & 26th COMAD), 2021
Modern AI systems are reaping the advantage of novel learning methods. With their increasing usage, we are realizing the limitations and shortfalls of these systems. Brittleness to minor adversarial changes in the input data, ability to explain the decisions, address the bias in their training data, high opacity in terms of revealing the lineage of the
Scott Burk, David E. Sweenor, Gary Miner
  +9 more sources

Towards Trustworthy AI in Dentistry. [PDF]

J Dent Res, 2022
Medical and dental artificial intelligence (AI) require the trust of both users and recipients of the AI to enhance implementation, acceptability, reach, and maintenance. Standardization is one strategy to generate such trust, with quality standards pushing for improvements in AI and reliable quality in a number of attributes.
Ma J, Schneider L, Lapuschkin S, Achtibat R, Duchrau M, Krois J, Schwendicke F, Samek W.   +7 more
europepmc   +3 more sources

Trustworthy AI: responses to commentators. [PDF]

Asian J Philos
Abstract In ‘Trustworthy Artificial Intelligence’, we develop a novel account of how it is that AI can be trustworthy and what it takes for an AI to be trustworthy. In this paper, we respond to a suite of recent comments on this account, due to J. Adam Carter, Dong-yong Choi, Rune Nyrup, and Fei Song. We would like to thank all four for their
Kelp C, Simion M.
europepmc   +3 more sources

Requirements for Trustworthy Artificial Intelligence and its Application in Healthcare [PDF]

Healthcare Informatics Research, 2023
Objectives Artificial intelligence (AI) technologies are developing very rapidly in the medical field, but have yet to be actively used in actual clinical settings.
Myeongju Kim, Hyoju Sohn, Sookyung Choi, Sejoong Kim   +3 more
doaj   +1 more source

Trustworthy artificial intelligence and ethical design: public perceptions of trustworthiness of an AI-based decision-support tool in the context of intrapartum care

BMC Medical Ethics, 2023
Background Despite the recognition that developing artificial intelligence (AI) that is trustworthy is necessary for public acceptability and the successful implementation of AI in healthcare contexts, perspectives from key stakeholders are often absent ...
Rachel Dlugatch, Antoniya Georgieva, Angeliki Kerasidou   +2 more
doaj   +1 more source

Of ChatGPT and Trustworthy AI

Journal of Human-Technology Relations, 2023
In this article, we examine whether ChatGPT is trustworthy and use our conversation with ChatGPT as a pivot for the larger conversation concerning trustworthy AI.  Through the example of our conversation with ChatGPT, we argue that the development of trustworthy AI requires keeping the best interests of users at heart.  In the process, we emphasize the
Puri, Anuj, Keymolen, Esther
openaire   +2 more sources

On Assessing Trustworthy AI in Healthcare. Machine Learning as a Supportive Tool to Recognize Cardiac Arrest in Emergency Calls

Frontiers in Human Dynamics, 2021
Artificial Intelligence (AI) has the potential to greatly improve the delivery of healthcare and other services that advance population health and wellbeing. However, the use of AI in healthcare also brings potential risks that may cause unintended harm.
Roberto V. Zicari, Roberto V. Zicari, James Brusseau, Stig Nikolaj Blomberg, Helle Collatz Christensen, Megan Coffee, Marianna B. Ganapini, Sara Gerke, Thomas Krendl Gilbert, Eleanore Hickman, Elisabeth Hildt, Sune Holm, Ulrich Kühne, Vince I. Madai, Vince I. Madai, Vince I. Madai, Walter Osika, Andy Spezzatti, Eberhard Schnebel, Jesmin Jahan Tithi, Dennis Vetter, Magnus Westerlund, Renee Wurth, Julia Amann, Vegard Antun, Valentina Beretta, Frédérick Bruneault, Erik Campano, Boris Düdder, Alessio Gallucci, Emmanuel Goffi, Christoffer Bjerre Haase, Thilo Hagendorff, Pedro Kringen, Florian Möslein, Davi Ottenheimer, Matiss Ozols, Laura Palazzani, Martin Petrin, Martin Petrin, Karin Tafur, Jim Tørresen, Holger Volland, Georgios Kararigas   +43 more
doaj   +1 more source

Ethics and Trustworthiness of AI for Predicting the Risk of Recidivism: A Systematic Literature Review

Information, 2023
Artificial Intelligence (AI) can be very beneficial in the criminal justice system for predicting the risk of recidivism. AI provides unrivalled high computing power, speed, and accuracy; all harnessed to strengthen the efficiency in predicting convicted
Michael Mayowa Farayola, Irina Tal, Regina Connolly, Takfarinas Saber, Malika Bendechache   +4 more
doaj   +1 more source

Supporting Trustworthy AI Through Machine Unlearning. [PDF]

Sci Eng Ethics, 2023
AbstractMachine unlearning (MU) is often analyzed in terms of how it can facilitate the “right to be forgotten.” In this commentary, we show that MU can support the OECD’s five principles for trustworthy AI, which are influencing AI development and regulation worldwide. This makes it a promising tool to translate AI principles into practice.
Hine E, Novelli C, Taddeo M, Floridi L.
europepmc   +6 more sources