It's easy to see why we need AI transparency. The question of how to build a transparent AI framework is much more complex. It will require collaboration among many different parties, including the operators, providers and consumers in emerging AI data and model marketplaces. Building AI transparency isn’t a single-point operation; it’s a journey with many steps. We’ll describe a few of the steps in that journey below.
AI models are already driving how decisions get made in enterprise settings, but there’s potential for them to do even more. Companies are looking for opportunities to apply AI to optimise every aspect of their operations, and they know they need to do it before the competition does.
One challenge preventing more AI adoption is a lack of transparency. Many modern AI models are essentially black boxes; no one can truly explain why they return the results they do. This fact limits the responsible application of AI across industries. For example, AI can help doctors make informed diagnoses quicker, but how can doctors trust AI models that they don’t truly understand? The bottom line is that enterprise-grade AI models won’t be considered viable unless they’re trusted, and they won’t be trusted unless they’re transparent.
