Automated Justice: Issues, Benefits and Risks in the Use of Artificial Intelligence and Its Algorithms in Access to Justice and Law Enforcement

Abstract

The use of artificial intelligence (AI) in the field of law has generated many hopes. Some have seen it as a way of relieving courts’ congestion, facilitating investigations, and making sentences for certain offences more consistent—and therefore fairer. But while it is true that the work of investigators and judges can be facilitated by these tools, particularly in terms of finding evidence during the investigative process, or preparing legal summaries, the panorama of current uses is far from rosy, as it often clashes with the reality of field usage and raises serious questions regarding human rights. This chapter will use the Robodebt Case to explore some of the problems with introducing automation into legal systems with little human oversight. AI—especially if it is poorly designed—has biases in its data and learning pathways which need to be corrected. The infrastructures that carry these tools may fail, introducing novel bias. All these elements are poorly understood by the legal world and can lead to misuse. In this context, there is a need to identify both the users of AI in the area of law and the uses made of it, as well as a need for transparency, the rules and contours of which have yet to be established.

Public Functions Implementation by Artificial Intelligence: Current Practices and Prospects for Common Measures within Particular Periods across Continents and Regions

“…Standing on the judicial practice, as well as using theoretical approaches in the field of the formation of predictive justice, French researchers formulate proposals for the purposes of AI-facilitated administrative justice and predictive justice in France: every AI that operates in the legal field, should have identified developers so that the results of its activity can be integrated into the adversarial process in the same way as other types of evidence; the nature of data processing and the calculation of AI indicators should be made public; there is also a strong requirement for publication of the sources, nature, and architecture of the data used to train the algorithms; the publication of areas of application of AI and the limiting contour of the impact of said technology for the litigation should be a must, as well [42].…”

The ethics of AI business practices: a review of 47 AI ethics guidelines

“…The actors and resourcing activities we associate with justice in AI value chains include: the public and private funding, procurement, data preparation, design, and development of automated decision-making systems with impacts on justice, as well as subsequent impacts of automated decisions on justice and public service outcomes (Angwin et al, 2016;Eubanks, 2018;Gans-Combe, 2022;Mulligan & Bamberger, 2019); the inclusion or exclusion of knowledge and perspectives from vulnerable, marginalized, and underrepresented groups in AI education, design, development, and governance processes, and particularly those who have been historically marginalized due to their race and/or gender (Birhane et al, 2022a;West, Whittaker, & Crawford, 2019); redistribution of resources required to develop and use AI systems, as well as the just or unjust distributions of value co-created throughout AI system lifecycles; macroscale social, political, and economic outcomes of widespread AI adoption (Dyer-Witheford, Kjøsen, & Steinhoff, 2019;Pasquale, 2020;Solaiman et al, 2023).…”

AI and professional liability assessment in healthcare. A revolution in legal medicine?

“…Although the use of AI in the forensic medical field for assessing professional responsibility has not been developed, AI's application has already been expanded within the legal context, and discussions about its potential use as evidence in civil and criminal cases have taken place (18)(19)(20).…”