Feb. 15, 2024, 5:43 a.m. | Jessica Zhu, Dr. Michel Cukier, Dr. Joseph Richardson Jr

cs.LG updates on arXiv.org arxiv.org

arXiv:2402.09286v1 Announce Type: cross
Abstract: Objective: Firearm injury research necessitates using data from often-exploited vulnerable populations of Black and Brown Americans. In order to minimize distrust, this study provides a framework for establishing AI trust and transparency with the general population. Methods: We propose a Model Facts template that is easily extendable and decomposes accuracy and demographics into standardized and minimally complex values. This framework allows general users to assess the validity and biases of a model without diving into …

abstract ai ethics ai trust arxiv cs.ai cs.lg data ethics facts framework general nutrition population practice research study transparency trust type violence vulnerable

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