Výsledky vyhledávání - "PARIKH, RAVI"

Report

Who's Gaming the System? A Causally-Motivated Approach for Detecting Strategic Adaptation

Autor: Chang, Trenton, Warrenburg, Lindsay, Park, Sae-Hwan, Parikh, Ravi B., Makar, Maggie, Wiens, Jenna

In many settings, machine learning models may be used to inform decisions that impact individuals or entities who interact with the model. Such entities, or agents, may game model decisions by manipulating their inputs to the model to obtain better o

Externí odkaz: http://arxiv.org/abs/2412.02000

Zobrazit plný text záznamu

Report

ACR: A Benchmark for Automatic Cohort Retrieval

Autor: Thai, Dung Ngoc, Ardulov, Victor, Mena, Jose Ulises, Tiwari, Simran, Erofeev, Gleb, Eskander, Ramy, Tarabishy, Karim, Parikh, Ravi B, Salloum, Wael

Identifying patient cohorts is fundamental to numerous healthcare tasks, including clinical trial recruitment and retrospective studies. Current cohort retrieval methods in healthcare organizations rely on automated queries of structured data combine

Externí odkaz: http://arxiv.org/abs/2406.14780

Zobrazit plný text záznamu

Report

DISCRET: Synthesizing Faithful Explanations For Treatment Effect Estimation

Autor: Wu, Yinjun, Keoliya, Mayank, Chen, Kan, Velingker, Neelay, Li, Ziyang, Getzen, Emily J, Long, Qi, Naik, Mayur, Parikh, Ravi B, Wong, Eric

Designing faithful yet accurate AI models is challenging, particularly in the field of individual treatment effect estimation (ITE). ITE prediction models deployed in critical settings such as healthcare should ideally be (i) accurate, and (ii) provi

Externí odkaz: http://arxiv.org/abs/2406.00611

Zobrazit plný text záznamu

Report

Towards Trustworthy Artificial Intelligence for Equitable Global Health

Autor: Qin, Hong, Kong, Jude, Ding, Wandi, Ahluwalia, Ramneek, Morr, Christo El, Engin, Zeynep, Effoduh, Jake Okechukwu, Hwa, Rebecca, Guo, Serena Jingchuan, Seyyed-Kalantari, Laleh, Muyingo, Sylvia Kiwuwa, Moore, Candace Makeda, Parikh, Ravi, Schwartz, Reva, Zhu, Dongxiao, Wang, Xiaoqian, Zhang, Yiye

Artificial intelligence (AI) can potentially transform global health, but algorithmic bias can exacerbate social inequities and disparity. Trustworthy AI entails the intentional design to ensure equity and mitigate potential biases. To advance trustw

Externí odkaz: http://arxiv.org/abs/2309.05088

Zobrazit plný text záznamu