{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T13:58:27Z","timestamp":1764251907467,"version":"build-2065373602"},"reference-count":17,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Canada Foundation for Innovation"},{"name":"Nova Scotia Research and Innovation Trust"},{"name":"Compute Canada Resource Allocation"},{"name":"NSERC Discovery"},{"name":"Nova Scotia Health Authority"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Traffic stops represent a crucial point of interaction between citizens and law enforcement, with potential implications for bias and discrimination. This study performs a rigorously validated comparative machine learning model analysis, creating artificial intelligence (AI) technologies to predict the results of traffic stops using a dataset sourced from the Montgomery County Maryland Data Centre, focusing on variables such as driver demographics, violation types, and stop outcomes. We repeated our rigorous validation of AI for the creation of models that predict outcomes with and without race and with and without gender informing the model. Feature selection employed regularly selects for gender and race as a predictor variable. We also observed correlations between model performance and both race and gender. While these findings imply the existence of discrimination based on race and gender, our large-scale analysis (&gt;600,000 samples) demonstrates the ability to produce top performing models that are gender and race agnostic, implying the potential to create technology that can help mitigate bias in traffic stops. The findings encourage the need for unbiased data and robust algorithms to address biases in law enforcement practices and enhance public trust in AI technologies deployed in this domain.<\/jats:p>","DOI":"10.3390\/info15110687","type":"journal-article","created":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T11:53:43Z","timestamp":1730462023000},"page":"687","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Mitigating Bias Due to Race and Gender in Machine Learning Predictions of Traffic Stop Outcomes"],"prefix":"10.3390","volume":"15","author":[{"given":"Kevin","family":"Saville","sequence":"first","affiliation":[{"name":"Department of Computer Science, St. Francis Xavier University, Antigonish, NS B2G 2W5, Canada"},{"name":"Department of Mathematics and Statistics, St. Francis Xavier University, Antigonish, NS B2G 2W5, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4733-0624","authenticated-orcid":false,"given":"Derek","family":"Berger","sequence":"additional","affiliation":[{"name":"Department of Computer Science, St. Francis Xavier University, Antigonish, NS B2G 2W5, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3162-3548","authenticated-orcid":false,"given":"Jacob","family":"Levman","sequence":"additional","affiliation":[{"name":"Department of Computer Science, St. Francis Xavier University, Antigonish, NS B2G 2W5, Canada"},{"name":"Nova Scotia Health Authority, Halifax, NS B3H 1V8, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,1]]},"reference":[{"key":"ref_1","unstructured":"Valentine, A. (2024). Car Ownership Statistics 2024, Forbes. Available online: https:\/\/www.forbes.com\/advisor\/car-insurance\/car-ownership-statistics\/."},{"key":"ref_2","unstructured":"(2024, February 12). The Stanford Open Policing Project. Available online: https:\/\/openpolicing.stanford.edu\/findings\/."},{"key":"ref_3","unstructured":"Davis, E., and Whyde, A. (2024, February 12). Contacts Between Police and the Public, 2015, Available online: https:\/\/www.bjs.gov\/content\/pub\/pdf\/cpp15.pdf."},{"key":"ref_4","unstructured":"Jeffords, S. (2024). Could More Red Light and Speed Cameras be Coming to Toronto Streets?, CBCnews. Available online: https:\/\/www.cbc.ca\/news\/canada\/toronto\/speed-cameras-system-change-1.7073593."},{"key":"ref_5","unstructured":"(2024, February 21). Speed Cameras by State 2024; World Population Review. Available online: https:\/\/worldpopulationreview.com\/state-rankings\/speed-cameras-by-state."},{"key":"ref_6","unstructured":"Reeves, F. (2023). New Speed Cameras \u201cPose a Threat to Everyone\u2019s Privacy\u201d with \u201cIntrusive\u201d 4D Technology, GB News. Available online: https:\/\/www.gbnews.com\/lifestyle\/cars\/new-speed-cameras-privacy-threat-4d-warning."},{"key":"ref_7","unstructured":"Raziel, B. (2023). A New App Seeks to Transform Traffic Stops, Forbes. Available online: https:\/\/www.forbes.com\/sites\/zengernews\/2023\/09\/22\/a-new-app-seeks-to-transform-traffic-stops\/?sh=21c47fd25d80."},{"key":"ref_8","unstructured":"Jany, L. (2023, August 23). Study to Use AI to Analyze LAPD Officers\u2019 Language During Traffic Stops. Police1. Available online: https:\/\/www.police1.com\/artificial-intelligence\/articles\/study-to-use-ai-to-analyze-lapd-officers-language-during-traffic-stops-XWGHUVwyTS3RcD9l\/."},{"key":"ref_9","unstructured":"Farooq, U. (2024). Police Departments are Turning to AI to Sift Through Millions of Hours of Unreviewed Body-Cam Footage, Route Fifty. Available online: https:\/\/www.route-fifty.com\/digital-government\/2024\/02\/police-departments-are-turning-ai-sift-through-millions-hours-unreviewed-body-cam-footage\/393876\/."},{"key":"ref_10","unstructured":"Rababah, M., Maydanchi, M., Pouya, S., Basiri, M., Norouzi Azad, A., Haji, F., and Aminjarahi, M. (2022). Data Visualization of traffic violations in Maryland, U.S. arXiv, Available online: https:\/\/arxiv.org\/pdf\/2208.10543."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Lee, J., Li, X., Mao, S., and Fu, W. (2021). Investigation of Contributing Factors to Traffic Crashes and Violations: A Random Parameter Multinomial Logit Approach, Online Library. Available online: https:\/\/onlinelibrary.wiley.com\/doi\/full\/10.1155\/2021\/2836657.","DOI":"10.1155\/2021\/2836657"},{"key":"ref_12","unstructured":"SherylWM (2024, February 12). SHERYLWM\/Descriptive-Analysis-of-Montgomery-Traffic-Violation-Data: Descriptive Analytics. GitHub. Available online: https:\/\/github.com\/sherylWM\/Descriptive-Analysis-of-Montgomery-Traffic-Violation-Data."},{"key":"ref_13","unstructured":"Abdella, A. (2022). Traffic Violation, Kaggle. Available online: https:\/\/www.kaggle.com\/code\/ahmedabdellahismail\/traffic-violation."},{"key":"ref_14","unstructured":"(2024, February 24). Traffic Violations: Open Data Portal, Available online: https:\/\/data.montgomerycountymd.gov\/Public-Safety\/Traffic-Violations\/4mse-ku6q\/about_data."},{"key":"ref_15","unstructured":"Stfxecutables (2024, February 24). Stfxecutables\/DF-Analyze. GitHub. Available online: https:\/\/github.com\/stfxecutables\/df-analyze."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Levman, J., Jennings, M., Rouse, E., Berger, D., Kabaria, P., Nangaku, M., Gondra, I., and Takahashi, E. (2022). A morphological study of schizophrenia with magnetic resonance imaging, advanced analytics, and machine learning. Front. Neurosci., 16.","DOI":"10.3389\/fnins.2022.926426"},{"key":"ref_17","unstructured":"Mutter, M. (2022). Drugs Related Violations, Kaggle. Available online: https:\/\/www.kaggle.com\/code\/michaelmutter\/drugs-related-violations-michael-s-project."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/11\/687\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:26:58Z","timestamp":1760113618000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/11\/687"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,1]]},"references-count":17,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2024,11]]}},"alternative-id":["info15110687"],"URL":"https:\/\/doi.org\/10.3390\/info15110687","relation":{},"ISSN":["2078-2489"],"issn-type":[{"type":"electronic","value":"2078-2489"}],"subject":[],"published":{"date-parts":[[2024,11,1]]}}}