TY  - JOUR
PY  - 2024//
TI  - Nutrition facts, drug facts, and model facts: putting AI ethics into practice in gun violence research
JO  - Journal of the American Medical Informatics Association
A1  - Zhu, Jessica
A1  - Cukier, Michel
A1  - Richardson, Joseph Jr
SP  - ePub
EP  - ePub
VL  - ePub
IS  - ePub
N2  - OBJECTIVE: Firearm injury research necessitates using data from often-exploited vulnerable populations of Black and Brown Americans. In order to reduce bias against protected attributes, this study provides a theoretical framework for establishing trust and transparency in the use of AI with the general population. <br><br>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 technical model documentation. EXAMPLES: We apply the Model Facts template on 2 previously published models, a violence risk identification model and a suicide risk prediction model. We demonstrate the ease of accessing the appropriate information when the data are structured appropriately. <br><br>DISCUSSION: The Model Facts template is limited in its current form to human based data and biases. Like nutrition facts, it will require educational programs for users to grasp its full utility. Human computer interaction experiments should be conducted to ensure model information is communicated accurately and in a manner that improves user decisions. <br><br>CONCLUSION: The Model Facts label is the first framework dedicated to establishing trust with end users and general population consumers. Implementation of Model Facts into firearm injury research will provide public health practitioners and those impacted by firearm injury greater faith in the tools the research provides.<p />  <p>Language: en</p>
LA  - en
SN  - 1067-5027
UR  - http://dx.doi.org/10.1093/jamia/ocae102
ID  - ref1
ER  -