Authored By: Adwoa Birago Oware-Mintah
Ghana School of Law
Abstract
The increasing use of artificial intelligence in legal systems is seen as a step toward greater efficiency and objectivity. There are tools like risk assessment algorithms and predictive policing models, which are supposed to reduce human error. However, this assumption is not as straightforward as it seems. These systems tend to reproduce, and in some cases even amplify, existing social inequalities embedded in the data they rely on.
This article looks at how bias enters Legal AI through historical patterns of discrimination reflected in datasets. It argues that the problem is deeper than technical error in the sense that even when algorithms appear neutral, their outcomes can still produce unfair results. This problem raises serious concerns about due process and equality before the law since the so-called “black box” nature of many systems only makes things worse because affected individuals may not even understand how decisions about them are made.
At the same time, defining fairness in this context is not easy. There is no single standard that works across all situations, and any attempt to fix bias may often involve trade-offs. Some approaches improve accuracy but worsen inequality, while others do the opposite. So the issue then is not just about correcting flawed systems, but also about deciding what fairness should mean in automated justice.
In the end, the article suggests that relying solely on technical solutions is not enough. Legal oversight, transparency requirements, and clearer regulatory frameworks are necessary, and without these, Legal AI risks reinforcing the very injustices it claims to solve instead of offering a meaningful improvement to the legal system.
Introduction
Artificial intelligence is gradually finding its place in legal systems across the world. What once sounded experimental is now being used in many practical ways. These include courts relying on risk assessment tools, police departments using predictive models, and even lawyers turning to automated systems for decision support. These systems promise speed, consistency, and, at least in theory, a kind of neutrality that human decision-making often lacks.[1]
The idea that machines are inherently objective is easy to accept but it is important to note that Legal AI systems do not operate in a vacuum. They are built on data, and that data reflects real-world patterns that are not always fair. In many cases, they are shaped by long-standing racial, economic, and social inequalities. Therefore, when these systems produce outcomes, they may simply be repeating what already exists, only now with a layer of technological authority.[2]
This creates a tension that is difficult to ignore because on one hand, there is a push toward innovation and efficiency in legal processes and on the other, there are foundational legal principles like fairness, equality before the law, and due process. When an algorithm influences a judicial decision, or even informs it, questions such as whether the system can be trusted or whether it can be challenged begin to arise.
The problem of bias in Legal AI is therefore not just technical but legal, ethical, and institutional. Some might argue that better design or cleaner data can solve the issue, but critics are less convinced because bias is not always visible, and even when it is, removing it is not straightforward. There are trade-offs involved, and sometimes fixing one kind of unfairness creates another.
This article argues that the rise of Legal AI requires a rethinking of what fairness means in the context of automated decision-making. It is not enough to assume that efficiency leads to justice; instead, there needs to be a more critical approach that recognizes the limits of these systems while still engaging with their potential. Without that, the legal system risks adopting tools that appear advanced, but quietly undermine the very principles they are meant to uphold.
Understanding Legal AI
Legal AI is a broad term that can mean slightly different things depending on the context. At its core, it refers to the use of artificial intelligence technologies to perform tasks that would normally require legal reasoning or human judgment. Sometimes this is straightforward, like document review or legal research, and other times it goes further into areas like predicting case outcomes or assessing the likelihood that someone will reoffend.[3]
A lot of these systems rely on machine learning, that is, they are trained on large datasets and learn patterns from past decisions. If the system has seen enough examples, it can make reasonably accurate predictions about new situations, but the simplicity is a bit misleading. What the system learns depends entirely on the data it is fed, and that data is often drawn from legal systems that are already imperfect.[4]
In practice, Legal AI shows up in a few key areas, and these include the use of risk assessment tools often used in bail or sentencing decisions. Predictive policing is also another approach, where algorithms try to identify where crime is more likely to occur. There are also decision-support systems that assist judges or lawyers by offering recommendations. These systems cannot fully replace human actors, but they can influence outcomes in ways that are not always obvious.
So, while Legal AI is often presented as neutral or data-driven, it is still shaped by human choices, specifically, what data to include, what outcomes to prioritize, and how the system is designed, and that is where some of the more difficult questions begin.
III. The Concept of Bias in AI Systems
Bias in AI is often talked about as if it is a single, clear problem, but it shows up in different ways, which is not immediately visible. In simple terms, bias refers to systematic errors that lead to unfair outcomes, but in the context of Legal AI, those errors are rarely random as they tend to follow existing social patterns.
One major source is data bias. AI systems are trained on historical data, and that data reflects past decisions. If those decisions were shaped by discrimination or unequal treatment, the system may learn and repeat those same patterns. For example, arrest records might overrepresent certain communities, not necessarily because of higher offending rates, but because of over-policing. The AI does not question this but rather it treats the data as fact.[5]
There is also algorithmic bias, which comes from how the system is designed. Choices about which variables to include, or how much weight to assign them, can affect outcomes in ways that are not always obvious. Even something that looks neutral like location data can act as a proxy for race or income level.[6]
Then there is human bias, which sits behind both the data and the design. Developers, institutions, and policymakers all make decisions that shape how these systems function so even if the technology appears objective, it is still influenced by subjective choices.All these forms of bias make it difficult to treat Legal AI as purely neutral. The issue is not just that bias exists, but that it can be built into the system in ways that are hard to detect and even harder to fix.
Case Studies and Real-World Examples
One of the most widely discussed examples is the COMPAS risk assessment tool used in parts of the United States. It is designed to predict the likelihood of reoffending and is often used to inform bail and sentencing decisions. However, investigations have suggested that the system disproportionately classified Black defendants as higher risk compared to white defendants.[7] What makes this more troubling is that the reasoning behind the scores is not fully transparent, which limits the ability to challenge them in court.
Predictive policing is another area where similar issues arise. These systems attempt to forecast where crime is likely to occur, often by analyzing past crime data. On the surface, this seems efficient, but if certain communities have historically been over-policed, the data will reflect that. The result is a feedback loop where more policing leads to more recorded incidents, which in turn justifies even more policing in the same areas.[8]
Facial recognition technology adds yet another layer to the problem. Studies have shown that these systems tend to have higher error rates when identifying individuals from certain demographic groups, particularly people with darker skin tones.[9] When used in law enforcement, this can lead to wrongful identification, which has obvious legal and ethical consequences.
All these examples suggest that bias in Legal AI is not just a possibility. It is already happening, and in ways that directly affect people’s rights and liberties.
Legal and Ethical Implications
The use of Legal AI raises issues that go beyond efficiency or accuracy. It starts to touch on core legal principles, and one of the main concerns is due process. If an algorithm plays a role in a legal decision, there is a question of whether the affected individual can properly understand or challenge that decision. In many cases, the systems are not transparent. They operate as what is often called a “black box,” making it difficult to explain how a particular outcome was reached.[10]
This lack of transparency links directly to fairness. The idea that like cases should be treated alike is central to most legal systems but if an AI system produces outcomes that disproportionately affect certain groups, even without explicit intent, it begins to clash with principles of equality and non-discrimination. The law does recognize indirect discrimination yet applying that concept to algorithmic systems is not always straightforward.
There is also the issue of accountability. When a biased or flawed decision is made, it is not always clear who should be held responsible. Is it the developer who designed discrimination, or the institution that adopted it, or the individual who relied on its output? The answer is not obvious, and current legal frameworks do not always provide clear guidance.
These concerns suggest that Legal AI cannot simply be treated as a neutral tool. Its use carries legal consequences, and it may even reshape how fundamental rights are understood and applied.
Rethinking Fairness in Automated Justice
Once bias in Legal AI is acknowledged, the next issue is harder because what exactly then counts as fairness in this context. Different definitions of fairness can point in completely different directions. Some approaches focus on treating everyone the same, while others argue that outcomes should be adjusted to account for existing inequalities.
In technical terms, there are multiple fairness metrics, and they do not always align. A system can be accurate overall but still produces unequal results across different groups, and trying to correct this can reduce accuracy or shift the bias elsewhere. So, there is no perfect solution waiting to be implemented. Instead, there are trade-offs, and those trade-offs are not just technical; they are normative decisions about what the law should prioritize.
This is where the limits of purely technical fixes become clear. Adjusting data or refining algorithms can help, but it does not resolve the deeper issue that Legal systems are built on values, not just outcomes. If those values are not clearly defined in the design and use of AI, the technology may end up reinforcing a narrow or incomplete idea of justice.
There is also the question of how much decision-making should be left to machines in the first place. Full automation might seem efficient, but it risks removing the human judgment that allows for context and discretion. A more realistic approach may be hybrid systems, where AI supports decision-making without fully replacing it.
In the end, rethinking fairness in automated justice is not just about improving systems. It is about deciding what kind of justice those systems are meant to deliver.
VII. Regulatory and Policy Responses
If Legal AI is going to remain part of the justice system, then the question shifts to regulation. At the moment, the legal framework is a bit uneven. Some existing laws do apply, especially in areas like data protection and anti-discrimination, but they were not designed with AI in mind, so applying them is not always clear.¹²
For example, data protection regimes such as the General Data Protection Regulation include ideas like transparency and the right to an explanation. On paper, that seems useful, but in practice, it is not always clear how much explanation an algorithm can realistically provide, especially when systems are complex or proprietary. This creates a gap between legal expectations and technical reality.
There have also been moves toward more specific AI regulation. Various guidelines and policy frameworks now emphasize principles like fairness, accountability, and transparency. These are important, but they tend to be quite general, and without clear enforcement mechanisms, they risk remaining more aspirational than practical.[11]
Many scholars argue for more concrete measures. Algorithmic audits are one option since they allow independent bodies to test systems for bias before and after deployment. Transparency requirements could also be strengthened, not just in terms of access to information, but in a way that is actually meaningful to those affected. There is also a case for involving a wider range of stakeholders in the design and oversight of these systems, rather than leaving decisions entirely to technical experts.
Ultimately, regulation in this area is still developing. The challenge is to strike a balance between encouraging innovation without allowing it to undermine basic legal protections.
VIII. The Future of Legal AI
Legal AI is unlikely to disappear, and its role will probably expand. Courts, law firms, and law enforcement agencies are already investing in these systems, and there is a sense that stepping back is not an option anymore. The more realistic question is how this technology will evolve, and what kind of legal system it will help shape.
There is some optimism around its potential. In theory, AI could help reduce inconsistencies in decision-making, improve access to justice, and handle large volumes of cases more efficiently. But that potential depends heavily on how the systems are designed and used. Without careful oversight, the same technology could just as easily deepen existing inequalities rather than resolve them.
Another issue is that different jurisdictions are likely to take different approaches. Some may prioritize innovation and adopt AI tools quickly, while others may take a more cautious, rights-based approach. This could lead to uneven standards, especially in areas like fairness and accountability. Over time, there may be pressure for more harmonization, but that is not guaranteed.
What seems clear is that Legal AI cannot be treated as purely a technical development. Its impact cuts across law, ethics, and public policy and because of that, responses to it will need to be interdisciplinary. Lawyers, computer scientists, policymakers, and even the public all have a role to play in shaping how these systems function.
The future of Legal AI is still open. It could become a tool for meaningful reform, or it could reinforce the very problems it was meant to solve.
Conclusion
Legal AI is often presented as a step forward for modern legal systems because it brings efficiency, consistency, and the ability to process information at a scale that would be difficult for humans alone. However, this article has shown that those advantages come with real concerns. Bias is not an occasional flaw in these systems ,it can be built into them from the start, shaped by the data they rely on and the choices made in their design.
What makes this particularly difficult is that the problem is not purely technical. Fixing biased data or improving algorithms may help, but it does not fully address the deeper issues around fairness and justice. Legal systems are not only about outcomes; they are also about principles and when automated systems begin to influence decisions, those principles can be harder to uphold in practice.
There is also a risk of over-reliance. The more these systems are used, the easier it becomes to treat their outputs as neutral or authoritative, even when they are not. This can quietly shift how decisions are made and justified. At the same time, completely rejecting Legal AI is not a realistic option, the technology is already embedded in many areas, and it will likely continue to develop.
So the challenge is not whether to use Legal AI, but how. This requires more than technical improvements. It calls for clearer legal standards, stronger oversight, and a willingness to question the assumptions behind these systems. Without that, there is a real possibility that automated justice will fall short of the fairness it is supposed to promote.
Reference(S):
[1] Richard Susskind, Online Courts and the Future of Justice (OUP 2019).
[2] Harris Mateen, ‘Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy: Cathy O’Neil. Broadway Books, 2016. , JSTOR https://www.jstor.org/stable/26732553 accessed 11 April 2026.
[3] Kevin D Ashley, Artificial Intelligence and Legal Analytics (CUP 2017).
[4] Solon Barocas and Andrew D Selbst, ‘Big Data’s Disparate Impact’ (2016) 104 California Law Review 671.
[5] Julia Angwin, Jeff Larson, Surya Mattu and Lauren Kirchner, ‘Machine Bias: There’s Software Used Across the Country to Predict Future Criminals. And It’s Biased Against Blacks’ (ProPublica, 23 May 2016) Machine Bias
[6] ibid
[7] Ibid
[8] Andrew Guthrie Ferguson, The Rise of Big Data Policing: Surveillance, Race, and the Future of Law Enforcement (NYU Press 2017).
[9] Joy Buolamwini and Timnit Gebru, ‘Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification’ (2018) 81 Proceedings of Machine Learning Research 1
[10] Frank Pasquale, The Black Box Society: The Secret Algorithms That Control Money and Information (Harvard University Press 2015).
[11] European Commission, Ethics Guidelines for Trustworthy AI (2019).
