Zobrazeno 1 - 10
of 321
pro vyhledávání: '"Jayadeva, P."'
Autor:
Hernández, Andrés Domínguez, Krishna, Shyam, Perini, Antonella Maia, Katell, Michael, Bennett, SJ, Borda, Ann, Hashem, Youmna, Hadjiloizou, Semeli, Mahomed, Sabeehah, Jayadeva, Smera, Aitken, Mhairi, Leslie, David
Publikováno v:
In Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency (FAccT '24). Association for Computing Machinery, New York, NY, USA, 776-796
Responding to the rapid roll-out and large-scale commercialization of foundation models, large language models, and generative AI, an emerging body of work is shedding light on the myriad impacts these technologies are having across society. Such res
Externí odkaz:
http://arxiv.org/abs/2407.17129
Autor:
Leslie, David, Rincon, Cami, Briggs, Morgan, Perini, Antonella, Jayadeva, Smera, Borda, Ann, Bennett, SJ, Burr, Christopher, Aitken, Mhairi, Katell, Michael, Fischer, Claudia
AI systems may have transformative and long-term effects on individuals and society. To manage these impacts responsibly and direct the development of AI systems toward optimal public benefit, considerations of AI ethics and governance must be a firs
Externí odkaz:
http://arxiv.org/abs/2403.15403
Autor:
Leslie, David, Rincon, Cami, Briggs, Morgan, Perini, Antonella, Jayadeva, Smera, Borda, Ann, Bennett, SJ, Burr, Christopher, Aitken, Mhairi, Katell, Michael, Fischer, Claudia, Wong, Janis, Garcia, Ismael Kherroubi
The sustainability of AI systems depends on the capacity of project teams to proceed with a continuous sensitivity to their potential real-world impacts and transformative effects. Stakeholder Impact Assessments (SIAs) are governance mechanisms that
Externí odkaz:
http://arxiv.org/abs/2403.15404
Autor:
Leslie, David, Rincon, Cami, Briggs, Morgan, Perini, Antonella, Jayadeva, Smera, Borda, Ann, Bennett, SJ, Burr, Christopher, Aitken, Mhairi, Katell, Michael, Fischer, Claudia, Wong, Janis, Garcia, Ismael Kherroubi
Reaching consensus on a commonly accepted definition of AI Fairness has long been a central challenge in AI ethics and governance. There is a broad spectrum of views across society on what the concept of fairness means and how it should best be put t
Externí odkaz:
http://arxiv.org/abs/2403.14636
Autor:
Leslie, David, Rincon, Cami, Briggs, Morgan, Perini, Antonella, Jayadeva, Smera, Borda, Ann, Bennett, SJ, Burr, Christopher, Aitken, Mhairi, Katell, Michael, Fischer, Claudia, Wong, Janis, Garcia, Ismael Kherroubi
Sustainable AI projects are continuously responsive to the transformative effects as well as short-, medium-, and long-term impacts on individuals and society that the design, development, and deployment of AI technologies may have. Projects, which c
Externí odkaz:
http://arxiv.org/abs/2403.14635
Ensuring fairness in Recommendation Systems (RSs) across demographic groups is critical due to the increased integration of RSs in applications such as personalized healthcare, finance, and e-commerce. Graph-based RSs play a crucial role in capturing
Externí odkaz:
http://arxiv.org/abs/2312.10080
Autor:
Sascha Keller, Ulrich Kunz, Ulrike Schmid, Jack Beusmans, Martin Büchert, Min He, Girish Jayadeva, Christophe Le Tourneau, Doreen Luedtke, Heiko G. Niessen, Zohra Oum’hamed, Sina Pleiner, Xiaoning Wang, Ralph Graeser
Publikováno v:
Journal of Translational Medicine, Vol 22, Iss 1, Pp 1-18 (2024)
Abstract Background BI 836880 is a humanized bispecific nanobody® that binds to and blocks vascular endothelial growth factor (VEGF) and angiopoietin-2 (Ang-2). A comprehensive biomarker and modeling approach is presented here that supported dose fi
Externí odkaz:
https://doaj.org/article/e16d3e8186014e00ab772fd03b57d4cc
Information extraction and textual comprehension from materials literature are vital for developing an exhaustive knowledge base that enables accelerated materials discovery. Language models have demonstrated their capability to answer domain-specifi
Externí odkaz:
http://arxiv.org/abs/2308.09115
The time evolution of physical systems is described by differential equations, which depend on abstract quantities like energy and force. Traditionally, these quantities are derived as functionals based on observables such as positions and velocities
Externí odkaz:
http://arxiv.org/abs/2307.05299
Neural networks (NNs) that exploit strong inductive biases based on physical laws and symmetries have shown remarkable success in learning the dynamics of physical systems directly from their trajectory. However, these works focus only on the systems
Externí odkaz:
http://arxiv.org/abs/2306.11435