From Readiness to Accountability
In early August, the initial governance obligations for General-Purpose AI (GPAI) models under the EU AI Act entered their first phase of applicability.
For the better part of the last eighteen months, in anticipation, the industry prepared in familiar ways. Consultancies sold “readiness frameworks,” legal teams drafted disclaimers, and vendors labeled slide decks as “compliant”. But as applicability has begun and supervisory scrutiny is increasing, a colder reality is emerging.
Quick example of a regulatory audit: a financial institution gets asked to explain why a transaction had been flagged and frozen six months earlier. The model version that produced the decision is no longer deployed. The prompt had changed. The original inference could not be replayed. What remains is a probability score without a defensible causal trace.
Compliance is not about promising safety.
It is about demonstrating it.
And probabilistic decisions are not reliably auditable.
The Black Box Problem
A fundamental issue facing enterprise AI today is accountability.
Modern Large Language Models (LLMs) are connectionist systems. They operate by learning statistical correlations across billions of parameters. When an LLM produces an output, it is not executing a formally defined rule or traversing an explicit chain of symbolic logic. It is sampling from a probability distribution to generate the most likely next token given its context.
When an LLM is asked why it produced a particular output, it does not return an executable trace of reasoning. It generates a new probabilistic response that resembles an explanation. This distinction is critical.
Auditing a model’s training data, validating its evaluation metrics, or applying post-hoc explainability techniques is not equivalent to auditing a specific decision made at a specific point in time. Model governance and statistical transparency do not, on their own, provide decision-level auditability.
In creative or exploratory contexts, this behavior is acceptable.
In regulated environments, such as finance, defense, or critical infrastructure, it introduces unacceptable uncertainty.
Imagine standing before a regulator or a court and explaining that a loan was denied, a security clearance revoked, or a supply chain route diverted because “the embeddings converged in high-dimensional space.”
That explanation may be technically accurate, but it is not operationally or legally sufficient.
The Glass Box Solution: Neurosymbolic Hybrid Engineering
At Evodant, we believe that addressing the black box problem does not require larger models, better prompting, or increasingly elaborate explanation layers. It requires a deliberate change in system architecture.
We advocate for a neurosymbolic hybrid approach.
Early neurosymbolic systems established an important foundation by separating perception from reasoning. In practice, however, high-assurance environments demand more than a simple handoff between neural and symbolic components. They require an architecture that integrates deterministic reasoning with policy enforcement, uncertainty handling, and authorization controls across the full decision lifecycle.
A neurosymbolic hybrid architecture preserves the core separation of concerns while extending it to meet real-world operational and regulatory constraints.
1. Neural Networks (The Perceptual Layer)
Neural systems excel at processing unstructured inputs: reading documents, interpreting sensor data, recognizing patterns in imagery, or transcribing audio. Their role is to perceive the environment and transform ambiguity into structured representations.
This layer may produce confidence scores, classifications, or extracted facts, but it does not make decisions. Its outputs are treated as inputs to downstream reasoning, not as authoritative conclusions.
2. Symbolic Reasoning (The Deterministic Core)
Once information is structured, it is passed to a deterministic, rule-based reasoning layer. This layer operates on explicit logic: if A and B, then C.
Business rules, regulatory constraints, and mission logic are encoded explicitly. Decision paths are enumerable, replayable, and inspectable. Given the same structured inputs and rules, the system will produce the same outcome.
This deterministic core is where accountability is enforced.
3. Hybrid Control Layers (Policy, Uncertainty, and Authorization)
In a neurosymbolic hybrid system, deterministic reasoning is augmented, but not replaced, by additional control layers that manage real-world complexity:
- Policy evaluation governs which rules apply under which operational contexts.
- Uncertainty handling ensures that ambiguous or low-confidence inputs are surfaced, constrained, or escalated rather than silently acted upon.
- Authorization controls enforce who, or what, is permitted to trigger or approve high-consequence actions.
These components are explicit, auditable, and configurable. They do not introduce probabilistic decision-making at the point of execution. Instead, they constrain and govern how deterministic logic is applied.
4. Explainability and Execution
Because decisions are produced through explicit rules operating on structured inputs under defined policies, the rationale for any outcome does not need to be inferred after the fact.
The system can identify:
- The specific evidence used
- The rules and policies evaluated
- The authorization state at the time of execution
- The sequence in which conditions were applied
Decision trees can be replayed, and governance constraints can be formally verified to have been enforced at the moment the decision was made.
This is what produces a true Glass Box architecture.
Engineering for Accountability
A neurosymbolic hybrid approach is not without cost. It requires explicit domain modeling, disciplined rule governance, and ongoing maintenance. These are not incidental burdens; they are the structural requirements of accountability in high-consequence systems.
Determinism does not emerge accidentally.
It is an engineered property of the system.
Moving Towards Verification
The EU AI Act is part of a broader convergence. Canada’s risk-based AI governance efforts under AIDA, NIST’s updated Risk Management Framework, emerging U.S. executive guidance, and evolving insurance underwriting practices are all signaling the same expectation.
Regulators are demanding traceability.
Insurers are demanding predictability.
Shareholders are demanding accountability.
If an AI-enabled system cannot reproduce and defend its past decisions without approximation, narrative reconstruction, or probabilistic inference, it will struggle to meet emerging compliance and risk standards, regardless of model sophistication.
A practical litmus test is simple:
If a regulator asked you today to replay yesterday’s AI-assisted decision, could you do so exactly, using the same inputs, rules, and constraints?
If your AI strategy depends on systems that explain themselves by generating plausible justifications after the fact, you do not have a governance strategy. You have unmanaged exposure.
It’s time to stop attempting to audit probabilities and start engineering determinism.