Ethical Implications of AI in Criminal Justice: Balancing Efficiency and Due Process
DOI:
https://doi.org/10.31305/rrijm.2024.v09.n07.014Keywords:
Artificial Intelligence, Criminal Justice, Due Process, Ethical Framework, Algorithmic BiasAbstract
The integration of Artificial Intelligence (AI) into criminal justice systems presents a complex landscape of opportunities and challenges. This research article critically examines the ethical implications of AI applications in various facets of criminal justice, from predictive policing to sentencing algorithms. While AI promises enhanced efficiency and data-driven decision-making, it simultaneously raises significant concerns about fairness, transparency, and the preservation of due process rights. This study employs a multidisciplinary approach, combining legal analysis, ethical philosophy, and empirical research to evaluate the current state of AI in criminal justice and project future trajectories. We analyze case studies from multiple jurisdictions, highlighting both successful implementations and controversial failures of AI systems in law enforcement and judicial processes. Our findings reveal a tension between the pursuit of efficiency through AI and the fundamental principles of due process. We identify several key ethical challenges, including algorithmic bias, lack of transparency in decision-making processes, and the potential erosion of human judgment in critical legal determinations. The research also uncovers disparate impacts on marginalized communities, raising concerns about the exacerbation of existing inequalities within the justice system. To address these challenges, we propose a comprehensive ethical framework for the development and deployment of AI in criminal justice. This framework emphasizes the need for continuous human oversight, regular audits of AI systems, and the establishment of clear accountability mechanisms. We argue for a balanced approach that leverages the benefits of AI while safeguarding individual rights and maintaining the integrity of judicial processes. The article concludes by outlining policy recommendations and best practices for lawmakers, judiciary, and law enforcement agencies. These recommendations aim to foster responsible innovation in criminal justice AI, ensuring that technological advancements align with ethical standards and constitutional protections. Our research contributes to the ongoing dialogue on AI ethics in the legal domain and provides a roadmap for the ethical integration of AI in criminal justice systems worldwide.
References
Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2016). Machine bias. ProPublica, 23, 2016.
Berk, R., & Hyatt, J. (2015). Machine learning forecasts of risk to inform sentencing decisions. Federal Sentencing Reporter, 27(4), 222-228.
Chouldechova, A. (2017). Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big data, 5(2), 153-163.
Green, B., & Chen, Y. (2019). Disparate interactions: An algorithm-in-the-loop analysis of fairness in risk assessments. In Proceedings of the Conference on Fairness, Accountability, and Transparency (pp. 90-99).
Kleinberg, J., Mullainathan, S., & Raghavan, M. (2016). Inherent trade-offs in the fair determination of risk scores. arXiv preprint arXiv:1609.05807.
Osoba, O. A., & Welser IV, W. (2017). An intelligence in our image: The risks of bias and errors in artificial intelligence. Rand Corporation.
Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206-215.
Wexler, R. (2018). Life, liberty, and trade secrets: Intellectual property in the criminal justice system. Stanford Law Review, 70, 1343.
Završnik, A. (2019). Algorithmic justice: Algorithms and big data in criminal justice settings. European Journal of Criminology, 17(5), 738-754.
Barocas, S., & Selbst, A. D. (2016). Big data's disparate impact. California Law Review, 104, 671-732.
Berk, R. A., Heidari, H., Jabbari, S., Kearns, M., & Roth, A. (2018). Fairness in criminal justice risk assessments: The state of the art. Sociological Methods & Research, 50(1), 3-44.
Brayne, S. (2017). Big data surveillance: The case of policing. American Sociological Review, 82(5), 977-1008.
Chiao, V. (2019). Fairness, accountability and transparency: Notes on algorithmic decision-making in criminal justice. International Journal of Law in Context, 15(2), 126-139.
Corbett-Davies, S., & Goel, S. (2018). The measure and mismeasure of fairness: A critical review of fair machine learning. arXiv preprint arXiv:1808.00023.
Desmarais, S. L., & Singh, J. P. (2013). Risk assessment instruments validated and implemented in correctional settings in the United States. Council of State Governments Justice Center.
Ferguson, A. G. (2017). The rise of big data policing: Surveillance, race, and the future of law enforcement. NYU Press.
Flores, A. W., Bechtel, K., & Lowenkamp, C. T. (2016). False positives, false negatives, and false analyses: A rejoinder to "Machine bias: There's software used across the country to predict future criminals. And it's biased against blacks.". Federal Probation, 80(2), 38-46.
Goel, S., Shroff, R., Skeem, J. L., & Slobogin, C. (2021). The accuracy, equity, and jurisprudence of criminal risk assessment. In Research Handbook on Big Data Law. Edward Elgar Publishing.
Hannah-Moffat, K. (2019). Algorithmic risk governance: Big data analytics, race and information activism in criminal justice debates. Theoretical Criminology, 23(4), 453-470.
Harcourt, B. E. (2015). Risk as a proxy for race: The dangers of risk assessment. Federal Sentencing Reporter, 27(4), 237-243.
Huq, A. Z. (2019). Racial equity in algorithmic criminal justice. Duke Law Journal, 68(6), 1043-1134.
Kearns, M., & Roth, A. (2019). The ethical algorithm: The science of socially aware algorithm design. Oxford University Press.
Kehl, D., Guo, P., & Kessler, S. (2017). Algorithms in the criminal justice system: Assessing the use of risk assessments in sentencing. Responsive Communities Initiative, Berkman Klein Center for Internet & Society, Harvard Law School.
Larson, J., Mattu, S., Kirchner, L., & Angwin, J. (2016). How we analyzed the COMPAS recidivism algorithm. ProPublica, 9(1).
Lepri, B., Oliver, N., Letouzé, E., Pentland, A., & Vinck, P. (2018). Fair, transparent, and accountable algorithmic decision-making processes. Philosophy & Technology, 31(4), 611-627.
Mayson, S. G. (2019). Bias in, bias out. Yale Law Journal, 128(8), 2218-2300.
Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 2053951716679679.
O'Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Broadway Books.
Pasquale, F. (2015). The black box society: The secret algorithms that control money and information. Harvard University Press.
Pleiss, G., Raghavan, M., Wu, F., Kleinberg, J., & Weinberger, K. Q. (2017). On fairness and calibration. In Advances in Neural Information Processing Systems (pp. 5680-5689).
Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J. (2019). Fairness and abstraction in sociotechnical systems. In Proceedings of the Conference on Fairness, Accountability, and Transparency (pp. 59-68).
Skeem, J. L., & Lowenkamp, C. T. (2016). Risk, race, and recidivism: Predictive bias and disparate impact. Criminology, 54(4), 680-712.
Starr, S. B. (2014). Evidence-based sentencing and the scientific rationalization of discrimination. Stanford Law Review, 66, 803.
Tolan, S., Miron, M., Gómez, E., & Castillo, C. (2019). Why machine learning may lead to unfairness: Evidence from risk assessment for juvenile justice in Catalonia. In Proceedings of the International Conference on Artificial Intelligence and Law (pp. 83-92).
Veale, M., Van Kleek, M., & Binns, R. (2018). Fairness and accountability design needs for algorithmic support in high-stakes public sector decision-making. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (pp. 1-14).
Whittaker, M., Crawford, K., Dobbe, R., Fried, G., Kaziunas, E., Mathur, V., ... & Schwartz, O. (2018). AI now report 2018. AI Now Institute at New York University.
Wachter, S., Mittelstadt, B., & Floridi, L. (2017). Why a right to explanation of automated decision-making does not exist in the general data protection regulation. International Data Privacy Law, 7(2), 76-99.
Yeung, K. (2018). Algorithmic regulation: A critical interrogation. Regulation & Governance, 12(4), 505-523.
Zafar, M. B., Valera, I., Gomez Rodriguez, M., & Gummadi, K. P. (2017). Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment. In Proceedings of the 26th International Conference on World Wide Web (pp. 1171-1180).
Završnik, A. (2020). Criminal justice, artificial intelligence systems, and human rights. ERA Forum, 20, 567-583.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
This is an open access article under the CC BY-NC-ND license Creative Commons Attribution-Noncommercial 4.0 International (CC BY-NC 4.0).
