Have you ever wondered whether you can really trust the answers provided by an AI? Many lawyers know the uneasy feeling: an AI delivers a seemingly brilliant legal answer — until it turns out that, for instance, Article 315 of Directive (EU) 2019/789 doesn’t actually exist. In practice, there have already been curious cases where AI tools simply invented non-existent rulings, laws, or paragraphs. Such “hallucinations” — made-up content — can have serious consequences. After all, lawyers rely on precision, verifiable sources, and facts, not on creative fake case law produced by a language model. It’s no surprise that many legal professionals remain skeptical about using AI in legal research.

The Black-Box Problem of Traditional Language Models

Why do AI language models hallucinate in the first place? The reason lies in their very nature as black boxes. Modern AI systems operate through an opaque web of neural networks. You input a question and receive an answer — but why that answer was generated often remains unclear. The internal decision-making processes are barely comprehensible to humans. As a result, the model may produce fluent, confident-sounding responses that are actually based on hallucinations or irrelevant training data — without the user noticing.

This lack of transparency understandably leads to uncertainty. If lawyers can’t trace how an AI arrives at its results, it’s difficult to trust its output in daily work. A model that behaves like a black box raises doubts about whether it might fabricate facts or cite false sources at a crucial moment. Confidence in AI systems erodes as long as they can’t explain their reasoning. Especially in the legal field — where accuracy and accountability are essential — this kind of black-box behavior is unacceptable. But does it have to stay that way?

Explainable AI: Turning the Black Box Transparent

The good news: there’s a way out of the black box. The magic words are Explainable AI (XAI). These methods make AI decisions understandable and traceable. Instead of blindly trusting an opaque model, XAI allows users to look “under the hood”: the AI can explain how it arrived at its answer. For lawyers, this means more control, more understanding — and no unpleasant surprises from fabricated content.

This is exactly where Anita, the AI research platform for lawyers, comes in. As a spin-off of the Fraunhofer Heinrich-Hertz-Institute and Freie Universität Berlin, Anita has built XAI into its core from the very beginning. Thanks to patented Fraunhofer software, Anita’s developers know precisely how the AI reaches its results — and can make those steps transparent. For users, this means: no more black box, no more hallucinations. Every AI answer is transparent and backed by sources rather than obscure “world knowledge.”

Neural Connections: Which Input Leads to Which Output?

So how does this traceability actually work? One approach is to analyze the neural connections inside the AI model. Put simply, the influence of specific inputs on outputs can be visualized. Similar to a legal opinion that explains every step of reasoning, an XAI analysis in Anita can reveal which keywords or facts in the question contributed to certain parts of the answer. In legal research, for example, one can see that mentioning a specific paragraph in the query — together with a matching sentence from a court decision — led the AI to cite that paragraph in its response. In other words, the AI shows which input caused which output.

The benefit is clear: lawyers can follow the AI’s reasoning step by step, ensuring that no hidden facts are being invented. However, such deep neuron analysis is computationally intensive. Examining the entire neural network for every answer requires significant processing power — and therefore time and cost. For live use, such as ongoing research, a more efficient method is needed. That’s where a special feature of modern language models comes in: attention heads.

Attention Heads: A Quick Check of Relevant Sources

Transformer-based AI models like GPT use so-called attention heads to keep track of text relationships. You can think of attention heads as different focus points within the AI: while generating an answer, the model uses multiple parallel “attention filters” to determine which parts of the input or underlying texts are most important. Each of these virtual attentions — or heads — focuses on a particular aspect.

Anita uses this principle to efficiently verify whether the model considered the relevant sources. In practice, the platform analyzes the weightings of attention heads to determine where the AI placed its focus while generating the answer. If a particular court decision was provided as context, Anita’s attention-head analysis can show whether — and how strongly — the model relied on that passage.

This process works almost in real time and at minimal cost, serving as an early-warning system against hallucinations. If the AI, when answering a legal question, overlooks the key text and instead draws on irrelevant general knowledge, this deviation would immediately show up in the attention-head patterns. Relevant sources are highlighted, while distractions are filtered out. This ensures that Anita bases its answers on legally authoritative sources rather than inventing content from thin air.

Deterministic Instead of Random: Same Question, Same Answer

Another advantage of explainable AI in Anita is its deterministic behavior. While some generative AI models respond differently each time to the same question (with slightly varied wording or focus), Anita prioritizes consistency: the same input always produces the same output — reliable and reproducible.

This is made possible by deliberately reactivating the same attention heads and internal pathways for identical queries. Simply put, Anita follows the same “thought process” every time the same question is asked. Random behavior and stochastic variations are switched off. Anita guarantees consistency. This determinism adds another layer of reliability: lawyers can trust that a solution found today will remain the same tomorrow — as long as the question and facts don’t change.

Conclusion: Transparency and Trust for Legal Professionals

Explainable AI transforms the former black box into a transparent, dependable digital colleague that lawyers can rely on. The AI research platform Anita demonstrates impressively that AI doesn’t have to be a threat to legal professionals — it can become part of the digital legal team. Thanks to XAI, Anita works without hallucinations and with verifiable sources — exactly what lawyers need to build trust. Instead of wasting time double-checking AI results or hunting for the proverbial needle in the haystack, you can rely on the AI’s precision and transparency and focus on what truly matters: delivering excellent legal advice and supporting your clients.

About the Author

Til Martin Bußmann-Welsch is Co-Founder and CXO of Anita, an AI platform that brings explainability and trust to legal research. With a background in law and technology, he focuses on building transparent AI systems that enable lawyers to understand, verify, and rely on machine-generated insights. He is also pursuing a PhD in judge analytics, exploring how judicial behavior can be analyzed through data. Before founding Anita, he co-founded iur.reform and contributed to several Legal Tech initiatives in Germany. His mission is to make AI a dependable partner in legal work — not a black box.