Predictive Policing and Ethical Implications in Forensic Science

Article Sidebar

PDF HTML
Published: Apr 1, 2025
DOI: https://doi.org/10.29070/6104qh22
Keywords:
Predictive Policing, Forensic, Investigation, Technology, Artificial Intelligence, Law, Justice

Main Article Content

Authors

Mrs. Shivani Saxena

M.sc In Forensic Science & Criminology, Shri Vaishnav Vidyapeeth Vishwavidyalaya, Indore, M.P.

Abstract

Predictive policing has become one of the most contentious uses of technology in law enforcement and forensic science. Predictive policing, grounded in data-driven approaches, aims to anticipate criminal activities utilizing algorithms, artificial intelligence (AI), and big data analytics. Proponents assert that these systems improve efficiency and resource allocation in law enforcement, while critics highlight issues of systematic bias, privacy violations, and the diminishment of civil freedoms. In forensic science, predictive policing signifies a transformative change in the interpretation of evidence, the profiling of suspects, and the administration of justice. This paper offers a thorough analysis of predictive policing, including its historical development, technological foundations, practical implementations, and, crucially, the ethical challenges it poses within forensic sciences. This paper critically assesses the relationship between algorithmic decision-making and forensic practice, evaluating the advantages and disadvantages of predictive policing while providing policy recommendations to reconcile security with justice, fairness, and accountability.

Downloads

Download data is not yet available.

Article Details

Issue

Vol. 22 No. 2 (2025): Journal of Advances in Science and Technology (JAST)

Section

Articles

References

  1. Ferguson, A. G. (2017). The Rise of Big Data Policing: Surveillance, Race, and the Future of Law Enforcement. New York University Press.
  2. Brayne, S. (2020). Predict and Surveil: Data, Discretion, and the Future of Policing. Oxford University Press.
  3. Perry, W. L., McInnis, B., Price, C. C., Smith, S., & Hollywood, J. S. (2013). Predictive Policing: The Role of Crime Forecasting in Law Enforcement Operations. RAND Corporation.
  4. Lum, K., & Isaac, W. (2016). To predict and serve? Significance, 13(5), 14–19.
  5. Richardson, R., Schultz, J. M., & Crawford, K. (2019). Dirty data, bad predictions. NYU Law Review Online, 94, 15–55.
  6. Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2016). Machine bias. ProPublica.
  7. Barocas, S., & Selbst, A. D. (2016). Big data’s disparate impact. California Law Review, 104(3), 671–732.
  8. Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J. (2019). Fairness and abstraction in sociotechnical systems. In Proceedings of FAT ’19* (pp. 59–68).
  9. Anantkumar, A., et al. (2025). AI-driven transformation of forensic investigations: Ethical limits, integrity risks, and mitigation pathways.
  10. Kumar, V., et al. (2025). AI in forensic science: Applications, challenges, and operational/ethical considerations.
  11. Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. Proceedings of FAT ’18, 77–91.
  12. Lexi. (2025). Toward an auditability-centered framework for AI-driven forensic validation and predictive policing ethics.
  13. Rudin, C. (2019). Stop explaining black box machine learning models for high-stakes decisions. Nature Machine Intelligence, 1(5), 206–215.
  14. Bharati, K. (2024). AI in criminal justice: Ethical tensions between efficiency and due process.
  15. National Research Council. (2009). Strengthening Forensic Science in the United States: A Path Forward. National Academies Press.
  16. Li, J. (2022). Predictive policing and the ethics of consequentialism, justice, and fairness.
  17. President’s Council of Advisors on Science and Technology (PCAST). (2016). Forensic Science in Criminal Courts: Ensuring Scientific Validity of Feature-Comparison Methods.
  18. Garvie, C., Bedoya, A., & Frankle, J. (2016). The Perpetual Line-Up: Unregulated Police Face Recognition in America. Georgetown Law, CSET.
  19. Mohler, G. O., Short, M. B., Brantingham, P. J., Schoenberg, F. P., & Tita, G. (2015). Randomized controlled field trials of predictive policing. PNAS, 112(12), 130–135.
  20. Brayne, S. (2017). Big data surveillance: The case of policing. American Sociological Review, 82(5), 977–1008.
  21. Meijer, A., & Wessels, M. (2019). Predictive policing: Review of benefits and drawbacks. Government Information Quarterly, 36(4), 101–117.
  22. Joh, E. E. (2019). Automating the right to remain silent? UCLA Law Review Discourse, 66, 99–119.
  23. Brayne, S., & Christin, A. (2020). Technologies of crime prediction. Annual Review of Criminology, 3, 321–345.
  24. Eubanks, V. (2018). Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. St. Martin’s Press.
  25. O’Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown.
  26. Zuboff, S. (2019). The Age of Surveillance Capitalism. PublicAffairs.
  27. Kleinberg, J., Mullainathan, S., & Raghavan, M. (2017). Inherent trade-offs in the fair determination of risk scores. Proceedings of ITCS ’17.
  28. Corbett-Davies, S., & Goel, S. (2018). The measure and mismeasure of fairness. In Proceedings of FAT ’18* (pp. 1–12).
  29. Skeem, J., & Lowenkamp, C. (2016). Risk, race, and recidivism. Law and Human Behavior, 40(6), 580–593.
  30. Dressel, J., & Farid, H. (2018). The accuracy, fairness, and limits of predicting recidivism. Science Advances, 4(1), eaao5580.
  31. Kehl, D., Guo, P., & Kessler, S. (2017). Algorithms in the Criminal Justice System: Assessing the Use of Risk Assessments. Berkman Klein Center.
  32. Brantingham, P. L., & Brantingham, P. J. (1995). Criminality of place and the crime generator/attractor dynamic. European Journal on Criminal Policy and Research, 3(3), 1–26.
  33. Sherman, L. W., Gartin, P. R., & Buerger, M. E. (1989). Hot spots of predatory crime. Criminology, 27(1), 27–56.
  34. Weisburd, D., Groff, E. R., & Yang, S.-M. (2012). The Criminology of Place. Oxford University Press.
  35. Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131.
  36. Lipton, Z. C. (2016). The mythos of model interpretability. In ICML WHI Workshop.
  37. Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms. Big Data & Society, 3(2), 1–21.
  38. Suresh, H., & Guttag, J. (2021). A framework for understanding sources of harm in ML. Proceedings of FAccT ’21, 695–706.
  39. Ensign, D., Friedler, S. A., Neville, S., Scheidegger, C., & Venkatasubramanian, S. (2018). Runaway feedback loops in predictive policing. In AIES ’18 (pp. 160–166).
  40. Roelofs, R., Cain, N., Shankar, V., et al. (2019). A meta-analysis of algorithmic bias in classification. In NeurIPS Workshop on Robust AI.