Community-Informed AI Models for Police Accountability
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910157039992832 |
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| author | Graham, Benjamin A. T. Brown, Lauren Chochlakis, Georgios Dehghani, Morteza Delerme, Raquel Friedman, Brittany Graeden, Ellie Golazizian, Preni Hebbar, Rajat Hejabi, Parsa Kommineni, Aditya Salinas, Mayagüez Sierra-Arévalo, Michael Trager, Jackson Weller, Nicholas Narayanan, Shrikanth |
| author_facet | Graham, Benjamin A. T. Brown, Lauren Chochlakis, Georgios Dehghani, Morteza Delerme, Raquel Friedman, Brittany Graeden, Ellie Golazizian, Preni Hebbar, Rajat Hejabi, Parsa Kommineni, Aditya Salinas, Mayagüez Sierra-Arévalo, Michael Trager, Jackson Weller, Nicholas Narayanan, Shrikanth |
| contents | Face-to-face interactions between police officers and the public affect both individual well-being and democratic legitimacy. Many government-public interactions are captured on video, including interactions between police officers and drivers captured on bodyworn cameras (BWCs). New advances in AI technology enable these interactions to be analyzed at scale, opening promising avenues for improving government transparency and accountability. However, for AI to serve democratic governance effectively, models must be designed to include the preferences and perspectives of the governed. This article proposes a community-informed, approach to developing multi-perspective AI tools for government accountability. We illustrate our approach by describing the research project through which the approach was inductively developed: an effort to build AI tools to analyze BWC footage of traffic stops conducted by the Los Angeles Police Department. We focus on the role of social scientists as members of multidisciplinary teams responsible for integrating the perspectives of diverse stakeholders into the development of AI tools in the domain of police -- and government -- accountability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_01703 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Community-Informed AI Models for Police Accountability Graham, Benjamin A. T. Brown, Lauren Chochlakis, Georgios Dehghani, Morteza Delerme, Raquel Friedman, Brittany Graeden, Ellie Golazizian, Preni Hebbar, Rajat Hejabi, Parsa Kommineni, Aditya Salinas, Mayagüez Sierra-Arévalo, Michael Trager, Jackson Weller, Nicholas Narayanan, Shrikanth Computers and Society Artificial Intelligence Machine Learning Audio and Speech Processing I.2.0; I.2.7 Face-to-face interactions between police officers and the public affect both individual well-being and democratic legitimacy. Many government-public interactions are captured on video, including interactions between police officers and drivers captured on bodyworn cameras (BWCs). New advances in AI technology enable these interactions to be analyzed at scale, opening promising avenues for improving government transparency and accountability. However, for AI to serve democratic governance effectively, models must be designed to include the preferences and perspectives of the governed. This article proposes a community-informed, approach to developing multi-perspective AI tools for government accountability. We illustrate our approach by describing the research project through which the approach was inductively developed: an effort to build AI tools to analyze BWC footage of traffic stops conducted by the Los Angeles Police Department. We focus on the role of social scientists as members of multidisciplinary teams responsible for integrating the perspectives of diverse stakeholders into the development of AI tools in the domain of police -- and government -- accountability. |
| title | Community-Informed AI Models for Police Accountability |
| topic | Computers and Society Artificial Intelligence Machine Learning Audio and Speech Processing I.2.0; I.2.7 |
| url | https://arxiv.org/abs/2402.01703 |