Artificial intelligence is increasingly used to make or inform decisions that affect people’s lives — from credit approvals and hiring processes to medical diagnoses and legal risk assessments. But many of the most powerful AI systems today cannot explain how they reach their conclusions. This tension between accuracy and explainability is not just a technical inconvenience: it is a legal and ethical problem with real consequences for individuals, organizations, and the legal professionals who advise them.
What Is the Black Box Problem?
When we talk about a „black box“ in AI, we mean a system that produces outputs – predictions, classifications, recommendations – without making its internal reasoning visible or understandable to the people using it or affected by it. You put data in, a result comes out, and what happens in between is opaque.
This is not a flaw unique to a few poorly designed systems. It is a structural feature of the most capable AI models in use today. Deep learning systems, including large language models, learn from enormous quantities of data by detecting patterns so complex that even their creators cannot fully trace the logic behind any given output. The model does not follow a set of written rules that can be audited. It operates through millions or billions of numerical weights, a kind of compressed statistical intuition that resists straightforward human interpretation.
The trade-off this creates is real: more complex models tend to be more accurate, but also less explainable. Simpler models – like decision trees or rule-based systems – are easy to interpret, but they often underperform in high-stakes predictive tasks. For years, this trade-off was largely accepted. Accuracy won. Explainability waited.
That calculation is now changing and the law is a significant reason why.
Why Explainability Is Now a Legal Requirement
The EU AI Act (Regulation 2024/1689) marks a turning point. Under Article 14, high-risk AI systems must be designed so that human overseers can understand the system’s capabilities and limitations and interpret its outputs meaningfully. Black-box models deployed in high-risk contexts – such as hiring, credit scoring, biometric identification, or access to essential services – must include explainability layers. Regulators and auditors are entitled to a human-readable account of how the system arrived at its decisions.
The transparency rules under Article 50 of the Act are set to come into full effect in August 2026. For organizations that have deployed AI in consequential decision-making roles, the question is no longer whether to address explainability – it is whether they can demonstrate compliance before enforcement begins.
The GDPR adds a further layer. Under Article 22, individuals have the right not to be subject to decisions based solely on automated processing that produce significant effects on them. Where such processing does occur, data subjects are entitled to an explanation, the ability to express their view, and the right to challenge the decision. AI systems that cannot be interrogated cannot satisfy these rights in any meaningful way.
In short: „the model said so“ is no longer a valid legal answer.
The Legal Profession's Own Black Box Problem
For lawyers, the stakes are both practical and professional.
On the practical side, legal professionals are increasingly relying on AI tools to conduct research, draft documents, flag risks, and assess litigation strategies. Many of these tools are built on the same opaque architectures that regulators are now scrutinizing. If a lawyer relies on an AI output they cannot explain or verify and that output turns out to be wrong, the professional responsibility question is not about the machine. Courts have made clear that ignorance of how an AI tool works is not a defense. Lawyers who submitted AI-generated case citations that turned out to be fabricated have been sanctioned, not the software providers – as the landmark Mata v. Avianca case (S.D.N.Y. 2023) made unmistakably clear.
On the advisory side, clients across industries are now asking legal counsel for guidance on how to deploy AI systems in compliance with emerging requirements. Advising on explainability obligations requires understanding what explainability actually means in technical terms, not just as a legal concept, but as an engineering one. Lawyers who cannot distinguish between post-hoc explanation techniques and intrinsically interpretable models are poorly equipped to help clients navigate that terrain.
Can the Trade-Off Between Accuracy and Explainability Be Resolved?
This is the central technical debate in the field and the answer is nuanced.
A growing discipline called Explainable AI (XAI) is developing tools to make opaque models more interpretable after the fact. Techniques like SHAP (SHapley Additive Explanations) assign contribution scores to each input variable, showing which factors most influenced a given prediction. LIME (Local Interpretable Model-Agnostic Explanations) approximates the behavior of a complex model around a specific prediction using a simpler, interpretable one.
These tools are genuinely useful, and they are already standard practice in regulated industries like financial services and healthcare. But they have limits. Post-hoc explanations describe a model’s behavior from the outside – they are approximations, not true accounts of the internal reasoning. Researchers have shown that these explanations can themselves be manipulated, and that they do not always capture what the model is actually doing.
A more recent and ambitious approach is mechanistic interpretability: attempting to reverse-engineer the internal computations of large AI models to understand, at a deeper level, what representations they are building and how decisions are flowing through them. This is active research at the frontier of AI science – promising, but far from producing practical compliance tools for most organizations today.
The practical implication for legal and compliance teams is that XAI methods should be seen as useful but not sufficient. They are a starting point for explainability, not a guarantee of it.
The Stakes in the Courtroom
Courts are also beginning to grapple directly with the black box problem. The question of how judges can meaningfully review a decision that was influenced by an AI system’s output – when neither the lawyers nor the judge can access the model’s internal reasoning – is one that legal systems are only beginning to answer.
In criminal sentencing contexts, AI risk assessment tools that predict recidivism have already been challenged on the grounds that defendants cannot meaningfully contest predictions they cannot understand. The argument is rooted in basic due process: if an algorithmic output contributes to a legal outcome affecting someone’s liberty, that person must have a meaningful opportunity to challenge it. The COMPAS sentencing algorithm in the United States became one of the most widely cited early cases of this tension, debated before the Wisconsin Supreme Court in State v. Loomis (2016), where the defendant argued, ultimately unsuccessfully, that relying on a proprietary algorithm he could not challenge violated his due process rights. It will not be the last.
For lawyers advising on the use of AI tools in litigation support, document review, or legal risk assessment, the inability to explain an AI’s output is not just a technical problem, it is a potential procedural one.
What Legal Professionals Should Take Away
The black box problem is not going away. The most capable AI systems available today, the ones that deliver the accuracy gains organizations are actually deploying, remain difficult to fully explain. Regulators are responding with transparency obligations that outpace current technical capacity in some areas, and harmonized standards under the EU AI Act that would clarify compliance methods have themselves been delayed.
For lawyers, this creates a clear professional imperative. Understanding explainability – what it means, what it requires legally, and where its technical limits lie – is no longer a specialist concern. It is foundational knowledge for anyone advising on AI deployment, handling AI-influenced legal processes, or relying on AI tools in their own practice.
The law has decided that consequential decisions require explanations. The challenge now is making sure that AI systems and the lawyers who use and advise on them, can actually provide them.
Final Thoughts
The tension between accuracy and explainability is one of the defining challenges of AI in high-stakes environments. Powerful AI systems earn their place by outperforming human judgment on complex predictive tasks, but that performance often comes at the cost of interpretability. Regulators have begun to draw a line: in contexts where AI decisions affect fundamental rights, employment, credit, or legal outcomes, opacity is not acceptable. For the legal profession, this is both a compliance challenge and a knowledge gap. Understanding the black box problem – not just as a regulatory concept but as a technical reality – is increasingly part of what it means to practice law in an AI-shaped world.
Stay curious, stay informed, and let´s keep exploring the fascinating world of AI together.
This post was written with the help of different AI tools.
