LEGAL STUDY ON ETHICAL ISSUES IN THE USE OF ARTIFICIAL INTELLIGENCE FOR LEGAL DECISIONS: CRITICAL LITERATURE REVIEW

Authors

  • Imam Hanafi Universitas Brawijaya
  • Kaharuddin Syah Universitas Muhammadiyah Palu
  • Loso Judijanto IPOSS Jakarta
  • Irma Rachmawati Maruf Universita Pasundan
  • Ihat Subihat Universitas Wiralodra Indramayu

DOI:

https://doi.org/10.31941/pj.v23i2.4659

Abstract

This comprehensive research embarks on meticulously exploring the intricate ethical nuances at the convergence of artificial intelligence (AI) and legal decision-making. Through an exhaustive literature review, the study meticulously navigates the complexities woven into algorithmic bias, the multifaceted dimensions of data privacy concerns, the profound implications on human agency, imperatives surrounding transparency, the socio-economic impacts stemming from the integration of AI, and the global perspectives that cast a profound influence on this intricate landscape. The synthesis of these insights reveals a dynamic interplay between the rapid evolution of technological capabilities and the intricate ethical considerations that underpin responsible AI integration into legal frameworks. The study underscores the need for ongoing interdisciplinary discourse, urging scholars, practitioners, and policymakers to engage in a continuous dialogue to ensure that ethical frameworks evolve in tandem with the relentless progression of AI technology. The conclusion advocates for a flexible and adaptive ethical framework poised to navigate the evolving ethical horizon, thereby ensuring AI's judicious and equitable integration into legal decision-making.

Downloads

Download data is not yet available.

References

Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2016). Machine Bias. ProPublica.

Barocas, S., & Selbst, A. D. (2016). Big Data’s Disparate Impact. California Law Review, 104(3), 671–732.

Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big data & society, 3(1), 2053951715622512.

Calders, T., Schermer, B., Zarsky, T., & Custers, B. (Eds.). (2013). Discrimination and Privacy in the Information Society: Data Mining and Profiling in Large Databases. Springer.

Chouldechova, A., & Roth, A. (2018). The frontiers of fairness in machine learning. arXiv preprint arXiv:1810.08810.

Citron, D. K., & Pasquale, F. (2014). The scored society: Due process for automated predictions. Wash. L. Rev., 89, 1.

Crawford, K., & Calo, R. (2016). There is a blind spot in AI research. Nature, 538(7625), 311-313.

Diakopoulos, N. (2016). Accountability in algorithmic decision making. Communications of the ACM, 59(2), 56-62.

Florida, L. (2019). AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations. Minds & Machines, 29(4), 689–707.

Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., ... & Vayena, E. (2021). An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Ethics, governance, and policies in artificial intelligence, 19-39.

Holzinger, A., Biemann, C., Pattichis, C. S., & Kell, D. B. (2017). What Do We Need to Build Explainable AI Systems for the Medical Domain? arXiv preprint arXiv:1712.09923.

Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389-399.

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.

Mittelstadt, B. D., Russell, C., & Wachter, S. (2019). I am explaining Explanations in AI. In Proceedings of the Conference on Fairness, Accountability, and Transparency (pp. 279-288).

O’Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Broadway Books.

O'neil, C. (2017). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown.

Pasquale, F. (2015). The Black Box Society: The Secret Algorithms That Control Money and Information. Harvard University Press.

Selbst, A. D., & Barocas, S. (2018). The Intuitive Appeal of Explainable Machines. Fordham Law Review, 87(3), 1085–1134.

Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J. (2019, January). Fairness and abstraction in sociotechnical systems. In Proceedings of the conference on fairness, accountability, and transparency (pp. 59-68).

Thurman, N., Lewis, S. C., & Kunert, J. (2019). Algorithms, automation, and news. Digital journalism, 7(8), 980-992.

Veale, M., Binns, R., & Edwards, L. (2018). Algorithms that remember: model inversion attacks and data protection law. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2133), 20180083.

Wachter, S., Mittelstadt, B., & Floridi, L. (2017). Why does a right to explain automated decision-making does not exist in the general data protection regulation? International Data Privacy Law, 7(2), 76-99.

Zarsky, T. (2016). The Trouble with Algorithmic Decisions: An Analytic Roadmap to Examine Efficiency and Fairness in Automated and Opaque Decision Making. Science, Technology, & Human Values, 41(1), 118–132.

Downloads

Published

2024-06-29