Building trust with AI transparency: benefits, challenges, and best practices

AI transparency is crucial for building trust in artificial intelligence systems, especially in customer-facing roles like loan officers. Transparency involves making computational results understandable to people, which includes understanding the logic applied, inputs and outputs involved, and how an algorithm arrived at a decision. Experts agree that when it comes to AI system output, transparency is not optional; it's required for building trust. This can be achieved by cultivating and maintaining customer trust, ensuring accountability, and promoting explainable AI. However, challenges with AI transparency include varying degrees of information understanding about systems' inner workings, the absence of a single agreed-on definition for "transparency," and evolving laws governing the technology's transparency, accountability, and other ethical aspects.