Zobrazeno 1 - 10
of 1 410
pro vyhledávání: '"GILBERT, THOMAS"'
Autor:
Yang, Qian, Wong, Richmond Y, Jackson, Steven J, Junginger, Sabine, Hagan, Margaret D, Gilbert, Thomas, Zimmerman, John
Policies significantly shape computation's societal impact, a crucial HCI concern. However, challenges persist when HCI professionals attempt to integrate policy into their work or affect policy outcomes. Prior research considered these challenges at
Externí odkaz:
http://arxiv.org/abs/2409.19738
Reinforcement learning from human feedback (RLHF) has emerged as a powerful technique to make large language models (LLMs) easier to use and more effective. A core piece of the RLHF process is the training and utilization of a model of human preferen
Externí odkaz:
http://arxiv.org/abs/2310.13595
Autor:
Casper, Stephen, Davies, Xander, Shi, Claudia, Gilbert, Thomas Krendl, Scheurer, Jérémy, Rando, Javier, Freedman, Rachel, Korbak, Tomasz, Lindner, David, Freire, Pedro, Wang, Tony, Marks, Samuel, Segerie, Charbel-Raphaël, Carroll, Micah, Peng, Andi, Christoffersen, Phillip, Damani, Mehul, Slocum, Stewart, Anwar, Usman, Siththaranjan, Anand, Nadeau, Max, Michaud, Eric J., Pfau, Jacob, Krasheninnikov, Dmitrii, Chen, Xin, Langosco, Lauro, Hase, Peter, Bıyık, Erdem, Dragan, Anca, Krueger, David, Sadigh, Dorsa, Hadfield-Menell, Dylan
Reinforcement learning from human feedback (RLHF) is a technique for training AI systems to align with human goals. RLHF has emerged as the central method used to finetune state-of-the-art large language models (LLMs). Despite this popularity, there
Externí odkaz:
http://arxiv.org/abs/2307.15217
Autor:
Yasar, Ayse Gizem, Chong, Andrew, Dong, Evan, Gilbert, Thomas Krendl, Hladikova, Sarah, Maio, Roland, Mougan, Carlos, Shen, Xudong, Singh, Shubham, Stoica, Ana-Andreea, Thais, Savannah, Zilka, Miri
As AI technology advances rapidly, concerns over the risks of bigness in digital markets are also growing. The EU's Digital Markets Act (DMA) aims to address these risks. Still, the current framework may not adequately cover generative AI systems tha
Externí odkaz:
http://arxiv.org/abs/2308.02033
Attention capitalism has generated design processes and product development decisions that prioritize platform growth over all other considerations. To the extent limits have been placed on these incentives, interventions have primarily taken the for
Externí odkaz:
http://arxiv.org/abs/2306.07443
Optimization is offered as an objective approach to resolving complex, real-world decisions involving uncertainty and conflicting interests. It drives business strategies as well as public policies and, increasingly, lies at the heart of sophisticate
Externí odkaz:
http://arxiv.org/abs/2305.17465
AI documentation is a rapidly-growing channel for coordinating the design of AI technologies with policies for transparency and accessibility. Calls to standardize and enact documentation of algorithmic harms and impacts are now commonplace. However,
Externí odkaz:
http://arxiv.org/abs/2303.10854
AI ethics is an emerging field with multiple, competing narratives about how to best solve the problem of building human values into machines. Two major approaches are focused on bias and compliance, respectively. But neither of these ideas fully enc
Externí odkaz:
http://arxiv.org/abs/2302.12149
Autor:
Zicari, Roberto V., Amann, Julia, Bruneault, Frédérick, Coffee, Megan, Düdder, Boris, Hickman, Eleanore, Gallucci, Alessio, Gilbert, Thomas Krendl, Hagendorff, Thilo, van Halem, Irmhild, Hildt, Elisabeth, Holm, Sune, Kararigas, Georgios, Kringen, Pedro, Madai, Vince I., Mathez, Emilie Wiinblad, Tithi, Jesmin Jahan, Vetter, Dennis, Westerlund, Magnus, Wurth, Renee
This report is a methodological reflection on Z-Inspection$^{\small{\circledR}}$. Z-Inspection$^{\small{\circledR}}$ is a holistic process used to evaluate the trustworthiness of AI-based technologies at different stages of the AI lifecycle. It focus
Externí odkaz:
http://arxiv.org/abs/2206.09887