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pro vyhledávání: '"A Ashurst"'
There has been a surge in the number and type of AI tools being tested and deployed within both national and local government in the UK, including within the social care sector. Given the many ongoing and planned future developments, the time is ripe
Externí odkaz:
http://arxiv.org/abs/2407.10244
Large Language Models (LLMs) have shown greatly enhanced performance in recent years, attributed to increased size and extensive training data. This advancement has led to widespread interest and adoption across industries and the public. However, tr
Externí odkaz:
http://arxiv.org/abs/2310.01424
Autor:
Zilka, Miri, Fogliato, Riccardo, Hron, Jiri, Butcher, Bradley, Ashurst, Carolyn, Weller, Adrian
Algorithmic risk assessment instruments (RAIs) increasingly inform decision-making in criminal justice. RAIs largely rely on arrest records as a proxy for underlying crime. Problematically, the extent to which arrests reflect overall offending can va
Externí odkaz:
http://arxiv.org/abs/2305.07575
Autor:
Butcher, Bradley, Robinson, Chris, Zilka, Miri, Fogliato, Riccardo, Ashurst, Carolyn, Weller, Adrian
Racial disparities in US drug arrest rates have been observed for decades, but their causes and policy implications are still contested. Some have argued that the disparities largely reflect differences in drug use between racial groups, while others
Externí odkaz:
http://arxiv.org/abs/2203.11771
In addition to reproducing discriminatory relationships in the training data, machine learning systems can also introduce or amplify discriminatory effects. We refer to this as introduced unfairness, and investigate the conditions under which it may
Externí odkaz:
http://arxiv.org/abs/2202.10816
Ethics statements have been proposed as a mechanism to increase transparency and promote reflection on the societal impacts of published research. In 2020, the machine learning (ML) conference NeurIPS broke new ground by requiring that all papers inc
Externí odkaz:
http://arxiv.org/abs/2111.01705
Autor:
Arbour, Sylvie, Asche, Carl, Ashurst, Carolyn, Balkanyi, Laszlo, Bennett, Hayley, Boros, Gerzson, Boyce, Rebecca, Carswell, Chris, Chaiyakunapruk, Nathorn, Chhatwal, Jagpreet, Ciani, Oriana, Collins, Gary, Dawson, David, Vanness, David, Di Bidino, Rossella, Faulding, Susan, Felizzi, Federico, Haig, Madeleine, Hawkins, James, Hiligsmann, Mikaël, Holst-Kristensen, Annette Willemoes, Isla, Julian, Koffijberg, Erik, Kostyuk, Alexander, Krief, Noemi, Lee, Dawn, Lee, Karen, Lundin, Douglas, Markiewicz-Barreaux, Katarzyna, Mauskopf, Josephine, Moons, Karel, Németh, Bertalan, Petrova, Guenka, Pwu, Raoh-Fang (Jasmine), Rejon-Parrilla, Juan Carlos, Rogers, Gabriel, Sampson, Chris, Springborg, Astrid Aaen, Steuten, Lotte, Sutherland, Eric, Suutala, Jaakko, Theisen, Daniel, Thompson, Alexander, van Gemert-Pijnen, Lisette, Walker, Thomas, Wilson, Ed, Elvidge, Jamie, Hawksworth, Claire, Avşar, Tuba Saygın, Zemplenyi, Antal, Chalkidou, Anastasia, Petrou, Stavros, Petykó, Zsuzsanna, Srivastava, Divya, Chandra, Gunjan, Delaye, Julien, Denniston, Alastair, Gomes, Manuel, Knies, Saskia, Nousios, Petros, Siirtola, Pekka, Wang, Junfeng, Dawoud, Dalia
Publikováno v:
In Value in Health September 2024 27(9):1196-1205
Autor:
Phillip L. Ealy, Crystal Tyler-Mackey, Kerri Ashurst, Misty Blue-Terry, Autumn Cano-Guin, Candi Dierenfield, Samantha Grant, Denae Harmon, Pamela B. Payne, Jennifer Wells-Marshall, Daniel F. Perkins
Publikováno v:
Frontiers in Public Health, Vol 12 (2024)
This case study on the Children, Youth, and Families At-Risk (CYFAR) Professional Development and Technical Assistance (PDTA) Center highlights a government-funded entity’s efforts to provide technical assistance to federal grantees of the CYFAR Su
Externí odkaz:
https://doaj.org/article/1cb2c265078e4c1f89cfb23acc5d759e
Autor:
Alex, Neel, Lifland, Eli, Tunstall, Lewis, Thakur, Abhishek, Maham, Pegah, Riedel, C. Jess, Hine, Emmie, Ashurst, Carolyn, Sedille, Paul, Carlier, Alexis, Noetel, Michael, Stuhlmüller, Andreas
Large pre-trained language models have shown promise for few-shot learning, completing text-based tasks given only a few task-specific examples. Will models soon solve classification tasks that have so far been reserved for human research assistants?
Externí odkaz:
http://arxiv.org/abs/2109.14076
Publikováno v:
Nature Machine Intelligence 3.2 (2021): 104-110
Turning principles into practice is one of the most pressing challenges of artificial intelligence (AI) governance. In this article, we reflect on a novel governance initiative by one of the world's largest AI conferences. In 2020, the Conference on
Externí odkaz:
http://arxiv.org/abs/2106.11039