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Key Takeaways

  • Beyond Post-hoc Explanation: Toward Glassbox AI via Probabilistic Mediation This paper addresses the growing problem of "black box" AI in high-stakes fields...
  • Existing approaches to explainability are predominantly post-hoc, offering unstable, non-contestable accounts that have no formal relationship to the reasoning process that produced the output.
  • We argue that the problem is not the absence of explanation but the absence of structured reasoning in the first place.
  • This paper makes the case for a fundamentally different architecture, which we call the Glassbox Framework, in which Bayesian networks serve as transparent, ante-hoc mediation layers for generative models.
  • Bayesian networks encode domain knowledge, causal assumptions, and probabilistic dependencies before inference occurs, enabling auditable reasoning traces, uncertainty quantification, and contestable outputs.
Paper AbstractExpand

Large language models are rapidly becoming infrastructural components in high-stakes institutional settings, including public administration, legal reasoning, and healthcare, where opacity is not merely inconvenient but institutionally and legally untenable. Existing approaches to explainability are predominantly post-hoc, offering unstable, non-contestable accounts that have no formal relationship to the reasoning process that produced the output. We argue that the problem is not the absence of explanation but the absence of structured reasoning in the first place. This paper makes the case for a fundamentally different architecture, which we call the Glassbox Framework, in which Bayesian networks serve as transparent, ante-hoc mediation layers for generative models. Bayesian networks encode domain knowledge, causal assumptions, and probabilistic dependencies before inference occurs, enabling auditable reasoning traces, uncertainty quantification, and contestable outputs. We characterise the architecture of this framework and ground it in a benefit eligibility scenario, identifying the foundational challenges spanning semantic alignment, dynamic model construction, probabilistic grounding, and human governance that must be solved to realise it at scale. By shifting from post-hoc explanation to ante-hoc probabilistic mediation, this work outlines a principled path toward AI systems that are not only powerful but fundamentally accountable.

Beyond Post-hoc Explanation: Toward Glassbox AI via Probabilistic Mediation
This paper addresses the growing problem of "black box" AI in high-stakes fields like healthcare, law, and public administration. The author argues that current methods of explaining AI—which attempt to interpret a model’s decisions after they have already been made—are fundamentally flawed because they are unstable and lack a formal connection to the system's actual reasoning. Instead, the paper proposes the "Glassbox Framework," an architecture that forces AI to use structured, transparent reasoning before it produces an output, ensuring that decisions are auditable and contestable from the start.

Moving from Post-hoc to Ante-hoc Accountability

The current industry standard for AI transparency is "post-hoc explainability," where a secondary tool is used to guess why a model made a specific choice. The author identifies three major failures in this approach: it is often unstable (small changes in input lead to wildly different explanations), it does not allow for genuine contestability (you are challenging an approximation, not the model itself), and it fails to provide a clear locus of responsibility. The Glassbox Framework shifts this paradigm to "ante-hoc" accountability, where the reasoning structure is defined and inspectable before the AI even begins its work.

The Role of Bayesian Networks

At the heart of the Glassbox Framework are Bayesian networks (BNs). Unlike the unstructured, probabilistic nature of large language models (LLMs), BNs represent knowledge as a directed graph where variables and their dependencies are explicitly mapped. By using a BN as a mediation layer, the system gains several critical features:

  • Native Uncertainty: Every inference results in a probability distribution rather than a single, opaque point prediction.

  • Direct Counterfactuals: Users can test "what-if" scenarios by intervening on specific variables within the graph.

  • Modularity: Domain-specific knowledge can be encoded into the BN without needing to rebuild the entire system, making it adaptable to different institutional settings.

How the Framework Operates

The framework functions through a continuous loop between an LLM and a BN. The LLM parses raw input and attempts to map it to the variables defined in the BN. If the BN detects that the LLM’s interpretation is inconsistent with the encoded domain rules, it triggers a targeted re-query, forcing the LLM to re-examine the data. This process continues until the system reaches a coherent, logically sound conclusion. The final output is not just a prediction, but a full, auditable trace of the reasoning path that led to that result.

Foundational Challenges

While the framework provides a path toward accountable AI, the author notes that it is not yet a "plug-and-play" solution. Several significant research hurdles remain, particularly regarding the "semantic alignment" between the fluid, continuous nature of human language and the rigid, discrete requirements of probabilistic models. Additionally, the system requires a robust governance layer—an institutional process for defining, auditing, and updating the BN—to ensure that the "glass box" remains accurate and aligned with legal and ethical standards as the environment changes.

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