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The interpretability of large language models (LLMs) is one of the most pressing challenges for their enterprise adoption. When a company uses a closed model like GPT or Gemini, it does not have direct access to its internal mechanisms, only to superficial outputs such as log-probabilities. This raises a key question: to what extent can we use open models as surrogates to explain the behavior of closed ones? A recent academic study introduces the concept of surrogate fidelity to address this issue.
The researchers evaluated the agreement between open models (Llama, Qwen) and closed ones at three levels: prediction, attribution, and representation. In binary classification tasks, they found that prediction fidelity—that is, both models giving the same answer—is misleading: models can agree on the what but radically disagree on the why. Causal attributions, obtained through input ablation, reveal that internal signals such as attention patterns or perturbation magnitudes do not predict the real causes of behavior well. This phenomenon, termed access validity inversion, demonstrates that transferring interpretative mechanisms from an open model to a closed one is not automatic.
For companies developing AI-based solutions, this limitation has direct implications in regulated fields such as healthcare, finance, or security. It is not enough for an artificial intelligence model to be correct; it is necessary to understand its reasoning to audit biases, ensure fairness, and comply with regulations like GDPR. This is where AI services for businesses from Q2BSTUDIO offer a pragmatic approach: we combine open and closed models with robust validation techniques, ensuring that interpretability is not sacrificed for accuracy.
Our experience in custom applications and custom software allows us to integrate explainability tools directly into workflows. Additionally, we deploy these solutions on scalable infrastructures using AWS and Azure cloud services, and we reinforce trust with cybersecurity audits that assess model integrity. For data-driven decision-making, we also offer business intelligence and Power BI services, which allow for clear visualization of model fidelity metrics.
The study underscores that simple agreement in predictions does not guarantee that an open model can explain a closed one. Therefore, at Q2BSTUDIO we design AI agents and hybrid systems that combine multiple sources of evidence, minimizing the risks of premature transfer. We understand that transparency is a strategic asset, and we work so that each client can trust their models, regardless of whether they are open or proprietary.
