AI makes justice more transparent – researchers

Introduction

Transparency in the justice system is central to ensuring legitimacy: people must believe that decisions are fair, that they understand how they are reached, and that there is accountability when things go wrong. Artificial Intelligence (AI) is increasingly being proposed and used as a tool to enhance transparency in legal systems — from helping judges and attorneys make more consistent decisions to making it easier for affected persons to know why and how certain outcomes were arrived at. But this promise comes with both opportunities and pitfalls. This essay explores what recent research shows about how AI can help make justice more transparent, the concerns raised, and what needs to be done for such transparency to be real and not just rhetorical.

How AI Can Enhance Transparency in Justice

1. Explainability and Interpretability (Explainable AI, XAI)
   • One of the critical issues with many AI systems is the “black box” nature: users and those subject to AI-made or AI-assisted decisions often can’t see why the system reached a particular conclusion. Techniques in Explainable AI aim to open up that box — letting stakeholders understand which features or pieces of evidence contributed to a decision.
   • There is increasing interest in “functional transparency” — that is, transparency not just of code or raw model internals, but of how the system behaves, how it was trained, what kinds of trade-offs were made (e.g. between fairness, accuracy, efficiency).
2. Algorithmic Decision-Making Can Reveal Patterns of Bias
   • Traditional human decisions (by judges, prosecutors, etc.) may carry implicit bias, or inconsistent application of rules that are hard to detect. AI systems, especially when trained on large datasets, can allow analysis of patterns across many decisions — making it easier to detect systematic unfairness. For example, in research “Discrimination in the Age of Algorithms,” scholars argue that algorithms may provide “crucial forms of transparency that are otherwise unavailable,” enabling better detection of discrimination.
   • In bail decision-making, researchers have shown that using predictive-models with proper evaluation can reduce crime rates without worsening racial disparities. Such empirical evaluation is only possible when the model’s behavior is well understood and monitored.
3. Public Perceptions, Procedural Justice, and Legitimacy
   • Transparency contributes to legitimacy: people are more likely to accept judicial outcomes when they believe they have been made through fair, open, and understandable processes. One recent study explored how the public perceives judges using AI tools (versus relying on human expertise alone) — in contexts like bail and sentencing. The study found that while judges relying on human expertise tend to be rated higher in legitimacy, there are significant differences across racial/ethnic groups; some minority groups (e.g. Black respondents in that study) expressed greater trust or perceived fairness in AI-assisted decision making, possibly because AI is seen as a way to reduce human bias in the system.
   • Another insight: transparency isn’t just about what the model does, but also about what people think when they see AI being used. If the purpose, features, and constraints of the AI are clear (e.g., which data are used, what trade offs are made), people are more willing to trust the system.
4. Accountability and Auditing
   • For AI-based systems to provide real transparency, there must be mechanisms for oversight, auditing, and recourse. This means external review of the algorithms, impact assessments, and possibly regulatory or legal requirements that force disclosure of how decisions are made or what mistakes have occurred.
   • Researchers emphasize that AI should augment rather than replace human decision makers, especially in high-stakes judgments, so that human accountability is preserved.

Challenges and Risks

While the potential is large, research also flags serious concerns that must be addressed to ensure that AI truly makes justice more transparent rather than obscuring or undermining it.

1. Opaque Models & Technical Complexity
   • Some models (e.g. deep neural networks) are inherently hard to interpret; efforts at explainability may produce simplified approximations that hide or gloss over complexity. This can lead to misleading or partial explanations.
   • Also, trade-offs exist: sometimes increasing explainability can reduce accuracy or performance. The challenge is to balance these in design and deployment.
2. Bias in Data and Prior Decisions
   • AI learns from past data. If datasets contain historical bias (racial, gender, socio-economic, etc.), then AI may reproduce or even amplify those biases. Transparency alone does not solve bias; bias mitigation, fairness constraints, and careful evaluation are needed.
3. Trust and Public Perception
   • Even if an AI system is transparent, or claims to be fair, people may distrust it — especially if they feel it lacks humanity or discretion, or worry about errors. The study on public perception showed that judges relying solely on human expertise were generally seen as more legitimate.
   • There can be racial or social group differences in how AI is viewed: some groups might see AI as a way to reduce human bias; others may worry that AI will codify or perpetuate systemic prejudice.
4. Accountability and Legal / Ethical Issues
   • Who is responsible if an AI-supported decision is wrong? Who monitors whether the AI is functioning properly, whether its inputs remain valid, etc.? Without clear oversight, transparency may be rhetorical rather than practical.
   • Privacy concerns: using personal data to train models, or to make decisions, raises issues about surveillance, consent, and data protection.
5. Implementation Gaps
   • Even when regulations or guidelines exist, deployment often lags. Tools may be proprietary, algorithms may be secret, and judicial systems may lack capacity (technical, legal, or financial) to audit or verify AI behavior.
   • Also, transparency demands are sometimes vague: what exactly must be disclosed, to whom, in what form, etc.? Research calls out the need for clearer definitions and standards.

Research Examples / Case Studies

• “Public Perceptions of Judges’ Use of AI Tools” (2025): This large‐scale study looked at how people from different racial/ethnic backgrounds perceive judicial legitimacy, fairness, trust when AI tools are used in bail or sentencing contexts. It provides empirical data on how transparency and fairness in AI usage affect public attitudes.
• Berkeley Law Study on legal aid attorneys: Found that AI tools significantly improve productivity of legal aid work, helping reduce justice gaps for low-income populations, but uptake was slower among women attorneys (until provided equal access to tools and training). This suggests transparency and access working together can help make the legal process more open and reachable.
• Discrimination in the Age of Algorithms: A theoretical examination of how algorithms can enable the detection of discrimination more easily than unstructured human decision-making, but also how algorithms themselves can hide bias if not carefully audited.

Recommendations: Making Transparency Real

To move from promise to practice, research and policy point to several necessary steps.

1. Clear Standards for Explainability and Transparency
   • Define what kinds of information must be disclosed: which features matter in a model, how decisions are made, what data was used, trade-offs, error rates.
   • Require model documentation, impact assessments, fairness audits, possibly verifiable external audits.
2. Inclusive Design & Stakeholder Participation
   • Involve people who are subject to the justice system (defendants, marginalized communities) in designing AI systems and deciding what transparency means for them.
   • Ensure that tools are accessible; for example, explanations should be in understandable language, not just technical or legal jargon.
3. Legal & Regulatory Frameworks
   • Laws or rules to require accountability for AI decision‐making in judicial or quasi-judicial contexts.
   • Mechanisms for redress when AI contributes to harm (e.g. wrongful convictions or sentencing errors).
4. Bias Mitigation & Data Quality
   • Ensuring training data is as unbiased and representative as possible.
   • Continuous monitoring for bias: checking outcomes for different demographic groups.
5. Human Oversight
   • AI should assist but not replace critical human judgment. Judges, attorneys, or other decision-makers must be able to review AI suggestions or outputs and overrule or question them if necessary.
   • Maintaining procedural fairness: people must have the opportunity to know what factors influenced a decision and possibly to contest or appeal them.
6. Transparency in Deployment and Audits
   • Public disclosure of the use of AI: when, in what ways, in which contexts.
   • Secrecy around important tools or models undermines trust. Independent evaluation and possibly open source or at least external auditing help.

Conclusion

AI holds substantial promise for making justice systems more transparent, consistent, and fair. By offering tools that expose patterns of bias, by providing explanations of how decisions are reached, and by increasing efficiency and access (especially for people who can’t afford extensive legal resources), AI can help strengthen public trust in the rule of law. However, this promise is not guaranteed: if AI systems are opaque, poorly regulated, or carry forward old systemic biases, they risk undermining legitimacy instead.

The key is to ensure that transparency is not only a design goal but baked into law, practice, and public participation. AI should be deployed with clear technical explainability, robust oversight, and with sensitivity to social context and justice. Only then can it truly help justice become more transparent — not just in appearance, but in lived reality.