The Rise of AI in Legal Decision-Making: Ethical and Regulatory Concerns

Introduction

The convergence of Artificial Intelligence (AI), and the law is no longer a speculative  possibility. Occurrences of AI impacting the legal decision-making process have begun  including predicting the outcome of legal matters or providing guidance in a sentencing  process. With machine learning tools making their way into the court system and law firms,  there is a sense of optimism but also trepidation. These tools may offer efficiency, savings,  and depth of analysis. Still, there are ethical and regulatory challenges with increasing use  over time. Can a machine truly know justice? Are there not significant opportunities for bias,  opacity, and erasing human discretion? 

The effectiveness of AI is at a critical juncture in a range of judicial and administrative  activities, particularly in the US, UK, and parts of the European Union. Factors related to  implications for regulating AI use in judicial processes mean regulators, scholars, and  practitioners must think about developments in two ways. From both an ethical perspective of  what AI-facilitated decision-making that amplifies human self-interest may implicate, and  regulatory action by government authorities to enable or restrict processes facilitated by AI.  We hope to reflect on the challenges posed by the ethical complexities of AI facilitated  decision-making and the regulatory approaches thus far as we move toward a much more AI enabled world. We contend AI can be extremely buoyant in social and human transformation,  but protections must be in place to speak to the rule of law, transparency, and humanity.

AI in Legal Decision-Making: An Overview

AI technologies deployed within legal decision-making have included predictive algorithms,  natural language processing (NLP), and machine learning models trained on historical case  information. Such technologies are used in legal environments ranging from legal research,  document review, sentencing, and risk assessments. Notable examples world-wide include  COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) (the  United States) in assessing recidivism risk and the use of judicial analytics through platforms  such as LexisNexis and ROSS Intelligence used by law firms.

AI is also beginning to encroach upon judicial discretion. An Estonian project (government backed) assessed AI’s use in adjudicating small claims (less than €7,000), while China has  integrated AI into its courts and other legal proceedings for legally-minded processes invited  from an AI, and to pilot anticipatory means of formalising a legal record. These  developments represent not just a departure for legal AI from an assistive role but moving to  a series of quasi-decision-making challenges to the conception of justice.

Ethical Concerns in AI Decision-Making

1. Algorithmic Bias and Discrimination

One of the most urgent issues is that AI systems can reinforce existing biases (and potentially  even magnify them) coded into legal data. AI models trained on historical case law or  sentencing records run the risk of encoding societal biases such as those generated by race,  sex, or class, into their systems. The COMPAS example has received significant criticism for  evidence of racial bias against African-American accused individuals when being labelled by  infringement of COMPAS, in which the programme showed greater false positive rates in  predicting future crimes as compared to white persons. It is often difficult to identify and  rectify such biases, especially when the algorithmic reasoning is obscured in ‘black box’  mode. This violates the principle of the equality of all persons before the law and risks  degrading the public’s confidence in the legal system.

2. Opacity and Lack of Explainability

Legal decisions require accountability, rational justification and process. Many AI systems,  especially deep learning systems, act as “black boxes” – decisions made on the basis of logic  not easily interpretable by humans. When logic fails to provide the necessary transparency, it  nullifies the rule of law– that justice needs to not only be done but be seen to be done.

An AI system that cannot explain the reasons why a defendant did not receive bail or a  harsher recommendation for their sentence fails the standards of procedural fairness and due  process. The European Commission argues that there are “serious potential risks regarding  accountability and transparency” where opacity is present in AI decision-making.

Dehumanisation of Justice

Legal decisions are never just about logic or efficiency: they involve moral reasoning,  choices about morality, empathy and discretion. To replace or over-rely on AI makes the  human justice system inhumane. In cases with vulnerable persons or multiplicity of social  context or multiple contending values, a human judge, unlike a human, can bring these values  to account.

Lastly, we must acknowledge that the use of AI could lead to a technocratic justice system  that prioritizes mathematical certainty rather than decisions based on holistic understandings.  AI could change significantly how a society views fairness and justice. Courts could change  into administrative engines rather than deliberative settings.

Regulatory and Legal Challenges

1. Lack of Uniform Standards

Despite Although AI is imposed into legal contexts quite quickly, there are still no coherent  legal frameworks established to regulate how AI can be used. Most jurisdictions still have not  enacted specific laws to regulate the use of AI in a judicial context or administrative decision  making. The European Union has proposed an Artificial Intelligence Act or regulation (still a  draft or proposed form) and is one of the first significant legislative proposals to characterise  AI applications by risk and then treat them accordingly. High-risk AI systems (including AI  used in legal decision making) will have strict requirements about transparency and  accountability to ensure there is always an objective person in the decision making, as well as having good quality data. However, the implementation steps and enforcement of standards  or measures are still uncertain. Furthermore, many regulators outside of the EU still do not  have measures to introduce similar standards.

       2.Accountability and Liability

When an AI system gets the law wrong by making a recommendation for an unfair sentence,  or decides not to grant an individual asylum, who is liable? The developer, the individual  agency that deploys the AI system, or the user? Current tort law and administrative law does  not enable legal accountability through an attribution of the diffusion of responsibility that is  created by AI systems. 

This gap in accountability also creates serious issues for legal professionals, as well as the  individuals experiencing the negative effects of an AI decision. When individuals suffer a  breach of a legal right without an attribution of responsibility, those individuals may not be  able to remedy the breach, and ultimately the purpose of having a legal system for redressing  wrongs will become futile.

3. Data Privacy and Security

AI systems are built on top of large datasets, many of which contain sensitive personal  information. The legal context includes criminal histories, immigration records, and health  information. Inappropriate handling of such data may result in a violation of privacy rights  under laws such as the UK GDPR and Article 8 of the European Convention on Human  Rights.

Furthermore, the potential for cyberattacks or the manipulation of AI models poses an  additional threat to the integrity of legal systems. Regulators must thus ensure that robust  cybersecurity and data protection measures are in place in tandem with the use of AI tools.

The Way Forward: Recommendations for Ethical AI Governance

To maximise AI’s benefits and minimise its risks, a complex ethical and regulatory  framework is required:

• Necessary Human Oversight: AI should assist human judges, not replace them. The ultimate legal decisions must always be made by accountable human actors who can interpret and override algorithmic outputs.
• Explainability and Transparency: Legal AI tools should adhere to explainability standards. Developers need to build models that can justify their decisions legally, ideally with interpretable machine learning techniques.
• Bias auditing and impact assessments: Regulators should mandate regular reviews of AI systems for possible discriminatory effects. Risk and impact assessments ought to be conducted both prior to and following deployment.

Conclusion

AI has the potential to significantly improve the effectiveness and uniformity of legal  systems. Yet, its increase in legal decision-making runs the risk of undermining the very  principles it aims to uphold—justice, equity, and accountability—in the absence of sufficient  ethical scrutiny and regulatory oversight. We must avoid delegating moral judgement to  machines as we enter a new era of legal technology. Rather, we need to create transparent, accountable, and human-reasoning legal AI systems. Only then can AI serve as a tool of  justice rather than a threat to it.

Footnotes:

1. Julia Angwin et al, ‘Machine Bias: There’s Software Used Across the Country to Predict Future Criminals. And It’s Biased Against Blacks’ ProPublica (23 May 2016) https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal sentencing accessed 28 May 2025.
2. European Commission, White Paper on Artificial Intelligence – A European approach to excellence and trustCOM(2020) 65 final.
3. European Commission, Proposal for a Regulation Laying Down Harmonised Rules on Artificial Intelligence (Artificial Intelligence Act) COM(2021) 206 final.