Responsible AI Starts with Transparency

Building a trusted approach to AI requires enterprises to track data provenance and lineage and build models in a way that gives users confidence and control

- EDDIE PETTIS AND HAIFENG LI

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 optimize 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.

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.

It All Starts with Data

Like any other aspect of AI, data is fundamental to transparency. We can't begin to explain the results of an AI model without first looking at the data used to train that model. The source and lineage of datasets are like a roadmap that can illustrate how a model ended up at a particular outcome.