The Futile Quest for AI Explanations
Government regulators and policymakers consistently demand one thing from artificial intelligence systems. They insist these advanced technologies must be explainable. This seems like a perfectly reasonable request on the surface. When an AI algorithm denies someone a crucial loan, misdiagnoses a medical condition, or takes autonomous action causing harm, affected individuals naturally want to know why.
However, forcing AI models to explain their internal reasoning presents an almost impossible challenge. The fundamental nature of how these systems operate makes traditional explanation methods ineffective.
Why AI Logic Defies Simple Explanation
Traditional software follows clear, logical steps written in code. When it fails, engineers can examine error messages and trace exactly where things went wrong. Neural networks work completely differently. Their "logic" distributes across billions of parameters in patterns humans cannot easily decipher.
An AI model's decision-making process resembles navigation through an enormous, hyper-dimensional data cloud. When you prompt a model, it converts your words into mathematical coordinates within this complex space. The system doesn't "read" your question like humans do. Instead, it locates your query within a landscape of relationships and connections.
Human communication operates in simple, linear text. To explain an AI decision, we must project these complex, multi-dimensional coordinates onto our one-dimensional understanding. This process inevitably loses crucial information. Imagine trying to describe a detailed three-dimensional sculpture using only shadow puppets on a wall. Now multiply that complexity exponentially.
The Limitations of Current Approaches
Some research labs attempt "mechanistic interpretability" to understand AI reasoning. This painstaking trial-and-error process identifies which specific neurons trigger particular responses in large language models. While valuable in limited contexts, this approach cannot scale effectively. AI models evolve too rapidly, and the method proves too time-consuming for practical oversight.
Most AI developers resort to having models generate their own explanations. They create Model Cards and System Summaries using AI itself to meet regulatory requirements. This creates another problem. AI systems excel at confabulation. Trained on human language and optimized to provide plausible responses, their explanations often tell humans what they want to hear rather than revealing true reasoning.
The situation worsens with multi-agent ecosystems. Decisions emerge from complex system interactions rather than single-entity logic. Demanding explanations in such environments represents a reductionist fallacy. You cannot understand a beehive's behavior by tracking one bee, just as you cannot explain agentic network outputs by examining individual nodes.
A Better Path: From Interpretability to Observability
So how do we ensure safety and accountability in increasingly influential AI systems? The solution lies in shifting focus from interpretability to observability. Instead of chasing elusive internal reasoning, we should monitor external behavior and outcomes.
This approach has proven successful in other complex engineering systems. Operators don't always understand every component's inner workings, but they recognize when systems begin to fail. Engineers establish "invariant" truths as guardrails. These are rules that must never be broken under any circumstances.
When systems cross these boundaries, operators receive immediate feedback for corrective action. We can implement similar frameworks for AI. Rather than understanding why an autonomous financial agent hedged a particular currency, we need assurance it operates within established risk thresholds. We must verify compliance with anti-money laundering regulations and other critical boundaries.
Implementing Effective AI Guardrails
Continuous monitoring and regular audits can enforce these invariants effectively. Autonomous systems should include mechanisms for human intervention when thresholds approach dangerous levels. This practical approach focuses on what truly matters: outcomes and performance.
This doesn't mean explanations have no value whatsoever. However, they will always remain insufficient for systems whose internal logic remains fundamentally unknowable. If governance efforts chase the ghost of perfect explainability, resulting systems will either become too slow for practical use or too dishonest to trust.
We must stop demanding that AI "explain" itself in human terms. Instead, we should require these systems to "prove" their reliability through consistent, verifiable outcomes. This outcome-focused accountability represents our most realistic path toward safe, trustworthy artificial intelligence.
The author is a partner at Trilegal and author of 'The Third Way: India's Revolutionary Approach to Data Governance.'
