Observations, empirical studies, and modern predictive systems confirm that judicial decisions made “solely according to the law and conscience” are, in practice, inevitably influenced by personal biases. Several factors impact the final verdict:
- The judge’s individual experience: their personal history, social circle, and professional habits;
- Personal values and beliefs, often unconscious;
- Random factors such as mood, fatigue, or external pressure;
- Collegial environment and “court culture,” where decisions are adapted to internal traditions or informal expectations.
Modern analytical tools like Pre/Dicta can predict case outcomes without analyzing the parties’ arguments—merely by assessing the judge’s profile (age, career, workplace, political or social views). If a lawsuit can be predicted based solely on the “judge’s portrait,” it raises a critical question: can such a justice system truly be called objective?
This doesn’t mean that all judges are consciously biased. Rather, we are dealing with a systemic phenomenon: the human brain is susceptible to heuristics, mental shortcuts, and what scientists refer to as “unconscious bias.” Unfortunately, once these distortions become embedded in judicial practice, they permeate the entire process.
Why an AI Judge Trained on Past Decisions Worsens the Problem
At first glance, replacing a human judge with a “neutral machine” seems appealing: a computer system trained on a large dataset of court cases is assumed to be free from subjectivity and capable of delivering faster, more “objective” justice. However, a closer analysis reveals that using artificial intelligence (AI) trained solely on the statistics of past rulings creates at least two significant problems.
1. Reproducing Historical Injustice: How “Hidden Patterns” Become a Threat to Justice
In many fields—such as marketing, medical diagnostics, and financial anti-fraud systems—big data analysis provides significant advantages. Algorithms can detect patterns that a human might overlook, improving decision-making efficiency. However, in the judicial system, these same “deep” patterns extracted from past rulings may do more harm than good. Instead of enhancing justice, they risk institutionalizing discrimination. When AI systems are trained on past court decisions, they often reproduce not fairness, but the very biases embedded in historical practices. This creates a serious threat to the integrity and fairness of the legal process.
The problem begins with the nature of the input data. Judicial decisions from the past were made within specific social, cultural, and historical contexts. If judges in a given region were historically harsher toward certain groups or more lenient with powerful individuals, these tendencies are absorbed into the AI’s training dataset. In areas like product recommendations, this kind of pattern detection may be useful. But in law, such “learning” can lead to the entrenchment of systemic distortions. Instead of identifying and correcting errors, the algorithm simply codifies and reinforces them.
A neural network trained on such biased data treats these distortions as statistical norms. It cannot distinguish between patterns that reflect fairness and those that reflect prejudice—it merely replicates what it has learned. The result is the automation of historical injustice, not its elimination.
This creates a dangerous illusion of objectivity. Machine learning tools that detect correlations and anomalies may offer valuable insights in business or healthcare. But in legal practice, what appears to be a powerful statistical finding—such as a strong correlation between certain demographic characteristics and unfavorable case outcomes—often reflects deeply rooted societal biases. The system “learns” that people with certain traits have historically lost cases more often, and it continues to reproduce this trend. What looks like a mirror of reality is, in fact, a distorted echo of injustice.
Worse still, AI can give these inherited biases the appearance of scientific legitimacy. When an algorithm shows a high correlation between group traits and case outcomes, many users may see this as objective truth. In reality, the AI is just aggregating decades of human prejudice and presenting it in the form of a technical “solution.” This makes it much harder to question or revise past injustices, because the output appears neutral and data-driven—even when it is built on flawed foundations.
The consequences extend beyond isolated errors. If one region or time period displayed systemic bias in its legal decisions, the impact was at least geographically or temporally limited. But when AI systems are trained on such data and deployed across entire jurisdictions—or multiple countries—localized discrimination becomes a widespread algorithmic norm. What was once a local flaw becomes a standard practice.
Furthermore, the lack of transparency in algorithmic decision-making makes correction difficult. A human judge’s reasoning can be appealed and scrutinized; legal errors can be challenged with reference to laws or precedents. But when a neural network functions as a “black box,” its internal logic is often opaque even to its creators. This makes it nearly impossible to detect or fix systemic errors—and much harder to prove that the system itself is biased. Unlike human judges, AI lacks accountability and the ability to justify its reasoning in legal terms.
In summary, any form of historical inequality—whether ethnic, social, or economic—captured in past court decisions is not only preserved by machine learning systems, but often amplified. The process of extracting hidden patterns from these decisions reinforces biases and gives them a broader reach and the appearance of scientific credibility. While in other domains such pattern recognition improves efficiency, in the legal system—where justice and human rights are paramount—it risks transforming injustice into a stable, algorithmic norm.
2. The Black Box — Opacity and the Illusion of Objectivity
One of the most serious limitations of using neural networks in the judicial sphere is their fundamental opacity. Modern algorithms—especially those based on deep learning architectures—operate with millions of interconnected parameters. As a result, even the developers of such models often cannot explain how a specific decision was reached by the algorithm.
In a traditional courtroom, the judge is obliged to justify their decision by referencing evidence, legal norms, and the logic behind their application. This creates a space for analysis, critique, and appeal. In the case of a neural network, such an opportunity is absent: the model does not explain why it selected one outcome over another—it simply outputs a result based on internal mathematical dependencies. This makes it impossible to conduct a proper causal analysis between the input data and the final decision.
Such systems are often perceived as inherently “objective” due to their technological sophistication. However, in practice, this sense of objectivity is an illusion. The algorithm may replicate biases inherited from historical data or follow hidden correlations that have no legal relevance. Yet the inner logic of the system remains inaccessible. Behind the appearance of neutrality lies a process that cannot be verified or legally challenged.
If a judge’s decision appears unfounded, it can be appealed by pointing to flaws in reasoning or misapplication of legal norms. Algorithmic decisions are different: their logic is unavailable to the parties involved and thus cannot be effectively contested. This creates a risk of decisions that cannot be understood, reviewed, or substantively appealed.
A judicial decision is not merely a technical outcome—it is a public affirmation of justice. It must be not only issued, but also reasoned, understood, and accepted by society. When participants in the legal process are unable to trace how a decision was reached, trust in the justice system erodes. Algorithmic justice that remains a “black box” risk losing social legitimacy, even if it appears efficient on the surface.
Why Reasoning Models and Explainable AI Don’t Solve the Problem
In recent years, fields like Explainable AI (XAI) and so-called reasoning models have emerged in an attempt to make algorithmic decisions more transparent. However, these approaches do not eliminate the core issue of opacity.
First, the majority of XAI solutions generate post hoc explanations, which only approximately interpret which features influenced the outcome. These explanations do not reveal the actual decision-making logic—they merely reconstruct the model’s probable behavior.
Second, such explanations are often oversimplified and fail to reflect the complex interactions between variables. This can create a false sense of understanding where none truly exists. Users receive a “readable” version of the reasoning, which often does not correspond to the model’s internal structure.
Third, reasoning models that attempt to embed reasoning capabilities within the architecture are still either too limited in legal contexts or technically incapable of replicating the complex, multilayered procedures of judicial analysis with sufficient accuracy, rigor, and logical consistency.
Finally, the fundamental issue remains: even with external explanation tools, the model itself—with its millions of parameters, weights, hidden layers, and interdependencies—remains inaccessible for verification. We still cannot guarantee that the algorithm isn’t relying on latent features with no legal relevance or simply reproducing historical patterns of discrimination.
What does the future hold for us?
From the standpoint of judicial technology evolution, we are faced with a choice:
- We can leave everything as it is—acknowledging human bias but justifying it by the fact that human errors can at least be publicly scrutinized and challenged.
- We can shift to “black box” systems trained on biased decisions, achieving only the illusion of efficiency.
- Or we can pursue a new approach: building a “thinking” AI judge based on clear algorithms and transparent procedures, where each step can be analyzed and corrected when necessary.
Rather than blindly replicating past experience—often unjust—we should rely on formalized rules and transparent logical structures. This way, we can preserve the best aspects of human justice—such as the ability to review and analyze decisions—while overcoming human weaknesses without falling into the trap of machine-driven discrimination. This path appears to be the most promising direction for the development of judicial AI—if our true goal is to enhance transparency and fairness in decision-making, rather than to entrench existing prejudices.
