Abstract
Racial inequities in police use of force has led scholars and policy makers to pursue ways to explain and reduce this gap. Many point to implicit bias in policing as a key driver in this disparity. However, there is limited evidence linking implicit cognitions and differences in behaviors. We argue that difficulties associated with recreating life-like situations with adequate experimental control in research settings designed to measure discriminatory behaviors obscure the relationship between implicit bias and behavior in policing. To address this, we rely on virtual reality to create experimental settings that better mimic officers’ experiences in the field. We evaluate the effectiveness of such a VR system in recognizing officers’ speech and correctly categorizing them. We find modest accuracy in both speech recognition and affective coding. However, the system has a significant and positive association with human generated codes for all but the most egregious cases of incorrect speech recognition. In quantifying and assessing the system’s effectiveness, we demonstrate VR’s potential in studying bias in policing.
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Acknowledgements
We thank David Johnson and Robert Koulish for contributions to the early design of the project. We also thank Clayton Buck, Racheli Cohen, Ellie Pokress, Nick Provenzano, Michael Ros, Wanda Segura Acevedo, and Olivia Sharon for excellent research assistance. This research was approved by the University of Maryland Institutional Review Board protocol no. 1363074.
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Doan, L. et al. (2021). Evaluation of a Virtual Reality Simulation Tool for Studying Bias in Police-Civilian Interactions. In: Schmorrow, D.D., Fidopiastis, C.M. (eds) Augmented Cognition. HCII 2021. Lecture Notes in Computer Science(), vol 12776. Springer, Cham. https://doi.org/10.1007/978-3-030-78114-9_26
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