Zobrazeno 1 - 10
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pro vyhledávání: '"GRAY, A. L."'
As generative AI technologies find more and more real-world applications, the importance of testing their performance and safety seems paramount. ``Red-teaming'' has quickly become the primary approach to test AI models--prioritized by AI companies,
Externí odkaz:
http://arxiv.org/abs/2412.09751
Behind the scenes of maintaining the safety of technology products from harmful and illegal digital content lies unrecognized human labor. The recent rise in the use of generative AI technologies and the accelerating demands to meet responsible AI (R
Externí odkaz:
http://arxiv.org/abs/2411.01426
Autor:
Zhang, Alice Qian, Shaw, Ryland, Anthis, Jacy Reese, Milton, Ashlee, Tseng, Emily, Suh, Jina, Ahmad, Lama, Kumar, Ram Shankar Siva, Posada, Julian, Shestakofsky, Benjamin, Roberts, Sarah T., Gray, Mary L.
Rapid progress in general-purpose AI has sparked significant interest in "red teaming," a practice of adversarial testing originating in military and cybersecurity applications. AI red teaming raises many questions about the human factor, such as how
Externí odkaz:
http://arxiv.org/abs/2407.07786
Publikováno v:
In The 2024 ACM Conference on Fairness, Accountability, and Transparency (FAccT '24), June 3-6, 2024, Rio de Janeiro, Brazil. ACM, New York, NY, USA, 13 pages
Growing interest and investment in the capabilities of foundation models has positioned such systems to impact a wide array of public services. Alongside these opportunities is the risk that these systems reify existing power imbalances and cause dis
Externí odkaz:
http://arxiv.org/abs/2405.19479
Variational approaches to disparity estimation typically use a linearised brightness constancy constraint, which only applies in smooth regions and over small distances. Accordingly, current variational approaches rely on a schedule to progressively
Externí odkaz:
http://arxiv.org/abs/2405.17029
Autor:
Charlton-Perez, Andrew J., Dacre, Helen F., Driscoll, Simon, Gray, Suzanne L., Harvey, Ben, Harvey, Natalie J., Hunt, Kieran M. R., Lee, Robert W., Swaminathan, Ranjini, Vandaele, Remy, Volonté, Ambrogio
There has been huge recent interest in the potential of making operational weather forecasts using machine learning techniques. As they become a part of the weather forecasting toolbox, there is a pressing need to understand how well current machine
Externí odkaz:
http://arxiv.org/abs/2312.02658
Autor:
Song, Bowen, Paolieri, Marco, Stewart, Harper E., Golubchik, Leana, McNitt-Gray, Jill L., Misra, Vishal, Shah, Devavrat
Objective: Our aim is to determine if data collected with inertial measurement units (IMUs) during steady-state running could be used to estimate ground reaction forces (GRFs) and to derive biomechanical variables (e.g., contact time, impulse, change
Externí odkaz:
http://arxiv.org/abs/2311.02287
Autor:
Chowdhary, Shreya, Kawakami, Anna, Gray, Mary L., Suh, Jina, Olteanu, Alexandra, Saha, Koustuv
Publikováno v:
2023 ACM Conference on Fairness, Accountability, and Transparency (FAccT '23), June 12--15, 2023, Chicago, IL, USA
Sensing technologies deployed in the workplace can unobtrusively collect detailed data about individual activities and group interactions that are otherwise difficult to capture. A hopeful application of these technologies is that they can help busin
Externí odkaz:
http://arxiv.org/abs/2303.07242
Autor:
Gray, Andrew L.1,2 (AUTHOR) graya1@ukzn.ac.za, Suleman, Fatima2,3 (AUTHOR)
Publikováno v:
Journal of Pharmaceutical Policy & Practice. Dec2024, Vol. 17 Issue 1, p1-16. 16p.
Autor:
Ghosh, Subrata, Nozariasbmarz, Amin, Lee, Huiju, Raman, Lavanya, Sharma, Shweta, Smriti, Rabeya B., Mandal, Dipika, Zhang, Yu, Karan, Sumanta K., Liu, Na, Gray, Jennifer L., Sanghadasa, Mohan, Xia, Yi, Priya, Shashank, Li, Wenjie, Poudel, Bed
Publikováno v:
In Joule 18 December 2024 8(12):3303-3312