Nutrition Facts, Drug Facts, and Model Facts: Putting AI Ethics into Practice in Gun Violence Research
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| Main Authors: | , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910582468247552 |
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| author | Zhu, Jessica Cukier, Michel Richardson Jr, Joseph |
| author_facet | Zhu, Jessica Cukier, Michel Richardson Jr, Joseph |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_09286 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Nutrition Facts, Drug Facts, and Model Facts: Putting AI Ethics into Practice in Gun Violence Research Zhu, Jessica Cukier, Michel Richardson Jr, Joseph Artificial Intelligence Machine Learning 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. |
| title | Nutrition Facts, Drug Facts, and Model Facts: Putting AI Ethics into Practice in Gun Violence Research |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2402.09286 |