Black Box Problem: AI Transparency and Explainability Are #1

Transparency vs. Explainability

These two concepts are often conflated but are meaningfully distinct. Transparency is about visibility into how a model operates: what data it was trained on, what its known limitations are, and how inputs are processed into outputs. Explainability goes further: it's about making the model's decision-making process comprehensible to a human, even a non-technical one.

Many of the most powerful AI systems, particularly deep learning models, are inherently opaque. They process information through hundreds of millions of parameters in ways that even their creators cannot fully trace. This "black box" nature creates compounding risks.

Without transparency, it becomes nearly impossible to audit a model for bias. If a hiring algorithm is systematically disadvantaging candidates from certain demographic groups, you can't identify the problem unless you can see inside the process. Amazon discovered this the hard way when they found that their AI resume screening tool was penalizing applications that mentioned women's colleges, because it had been trained predominantly on resumes submitted by men.

The Accountability Gap

When AI makes a consequential decision, and something goes wrong, accountability becomes murky. Is the vendor responsible? The developer? Is the organization deploying the tool? Without clear documentation of how the model works and how decisions are made, resolving that question, legally or practically, is extremely difficult.

Risk Management Best Practices

Vendors should maintain documentation for every AI model in use, including the model's function, its training data sources, its known limitations, and performance metrics. Explanations of AI decisions should be provided in business language, not just technical outputs. Methods such as feature importance mapping, natural language justifications, and saliency maps should be used to make model reasoning more accessible.

From a TPRM perspective, the contract language with your AI vendors should include requirements for transparency mechanisms, audit trails, and accountability processes in the event the model fails to perform as expected.

Coming up next: Adversarial attacks: when bad actors use AI against AI.