Explainability: The Word Generative AI Forgot
Explainability has long been a cornerstone of predictive modeling, but it has been largely drowned out in the enterprise rush toward generative AI. At its core, explainability is our ability to understand why a model produces a specific outcome. Without it, we cannot reliably interpret, predict, or defend its decisions.
Start with the simplest predictive model: linear regression. Plot house prices against square footage, draw a best-fit line through the points, and the underlying logic is immediately visible. Bigger houses cost more, and the slope reveals by how much. To predict a price based on size, you simply follow the line.
Figure 1: Linear Regression
Add more inputs—bedrooms, ZIP code, age of the property—and multiple linear regression yields greater predictive power. While you can no longer plot it on a single two-dimensional chart, it remains fully explainable. Every input carries a distinct coefficient—a weight quantifying its impact on the prediction while all other factors remain constant. You can inspect the model and state unequivocally: “This is why.”
Deep neural networks, the bedrock of modern AI, changed all of that. A neural network consists of layered mathematical units interconnected by weights learned from training data. When scaled across dozens of layers and millions of parameters, tracing the exact non-linear decision path from input to output becomes practically impossible.
Figure 2: Neural Network
Today, transformers are the architecture powering modern large language models and add self-attention mechanisms that calculate how every token relates to every other token. Frontier models like Claude and ChatGPT operate across hundreds of billions of parameters. No individual parameter carries standalone meaning; the model’s “reasoning” is distributed across the entire network in superposition. That makes it a true black box.
Frontier AI labs have attempted to bridge this divide. Models now output visible “thinking” steps before generating an answer. While these reasoning traces are useful, they are not a diagnostic telemetry feed of the model’s internal weights. As Anthropic’s own research on chain-of-thought faithfulness revealed, reasoning models frequently rely on latent variables and biases they never disclose in their published thoughts. In short: the explanation generated is not guaranteed to be the calculation that occurred.
In mission-critical enterprise environments, this creates severe operational and legal vulnerabilities:
- Can we guarantee the system reaches consistent outcomes given identical conditions?
- Who carries liability when a hallucination triggers a catastrophic downstream action?
- Can we satisfy an auditor, regulator, or impacted customer?
Regulators are already drawing hard lines. The EU AI Act imposes strict transparency and human-oversight mandates on high-risk systems, while U.S. consumer protection laws (such as the Equal Credit Opportunity Act) require lenders to issue precise adverse action reasons, irrespective of algorithmic complexity.
There is a better architectural approach: instead of trying to reverse-engineer explainability into an opaque runtime black box, keep the black box out of the execution layer entirely. This is why Pega focuses generative AI primarily at design time. Through tools like Pega Blueprint, GenAI assists in synthesizing requirements, drafting case lifecycles, data models, and business rules. Enterprise teams review and refine these assets in an intuitive visual medium. Once deployed, what executes is not a probabilistic prompt chain, but a deterministic, auditable workflow that runs predictably every time and remains easily configurable by business operators.
By pairing generative speed at design time with deterministic execution at runtime, enterprises capture the agility of modern AI without sacrificing the audit-ability that enterprise governance demands. The AI handles the creative heavy lifting; the system that runs stays completely explainable.