AI's Black Box Problem

You may have heard that much of modern AI is a black box. What does this mean? Typically it's explained as being a system where you can see the input and output, but the internal decision-making process is either too complex or too opaque to understand completely. This makes it sound like it's a sealed box where you can see what goes in and what comes out, but can't see how it transforms one into the other.

But this is misleading because we actually can see inside the system. We can examine every weight, parameter, and connection. The problem isn't that the internals are hidden; it's that they're too complex for us to meaningfully interpret.

Modern AI is fundamentally different from traditional computer programs. Traditional computing follows clear, step-by-step instructions that we can trace. But AI, particularly neural networks, process information a lot like the human brain. Billions of connections and thousands of layers, all working together to create the illusion of intelligence. When an AI makes a decision, the process involves so many simultaneous calculations that documenting them would be futile. And we can't just simplify without losing critical information that influenced the final decision.

The Challenge for Organisations

This creates serious practical challenges for organisations using AI for sensitive decision-making. They face a choice between three poor options: document the incomprehensible, provide simplified explanations that miss important details, or skip explanations entirely and judge AI solely on outcomes. Organisations face increasing regulatory requirements that demand transparency. Their customers want transparency in order to trust them. And they need to manage increasing legal risks from AI decisions.

We've long operated under the assumption that machines should be predictable, deterministic, and comprehensible. AI systems force us to accept a new paradigm where power and complexity come at the cost of complete understanding. It's a trade-off that nature made long ago - our most sophisticated biological systems sacrifice simplicity for capability. The question isn't whether we should accept black box systems, but how to establish appropriate guardrails for their deployment. When we trial new medicines, we don't need to understand every molecular interaction to know the drug is safe and effective. What we need are comprehensive frameworks for testing AI systems across diverse scenarios, with clear success metrics and failure conditions.

We may need to embrace the complexity of modern AI rather than fight it.