Nutrition Facts, Drug Facts, and Model Facts: Putting AI Ethics into
  Practice in Gun Violence Research
Jessica Zhu Dr. Michel Cukier Dr. Joseph Richardson Jr
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
technical model documentation. Examples: We apply the Model Facts template on
two 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 is structured appropriately. Discussion:
The Model Facts template is limited in its current form to human based data and
biases. Like nutrition facts, it also will require some educational resources
for users to grasp its full utility. Human computer interaction experiments
should be conducted to ensure that the interaction between user interface and
model interface is as desired. 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.