What is Auditability for AI Systems?

Up until recently, we accepted the “black box” narrative surrounding AI as a necessary evil that could not be extrapolated away from AI as a concept. We understood that tradeoffs were sometimes necessary to achieve performance accuracy at the expense of transparency and explainability.

Fortunately, there have been advancements in the last few years that make it technologically feasible to explain why AI models reach decisions, which represents an inflection point for the future of this transformative technology.

In order for AI to become mainstream, we must have transparency and insight into how AI-enabled decisions were reached—meaning we should be able to pinpoint what factors contributed to a decision.

Today’s approach is one full of risk as organizations rely on trusting already overburdened data scientists to document and explain their work. This approach does not lend itself to adapt to the scale required for AI at enterprise scale and requires a lot of extra time wasted on explanation. Instead, organizations should look to adopt tools that allow for auditability to track AI behavior and usage over time within an organization.

ModelOps tools are emerging as a new way to monitor and manage AI models’ performance while providing the ability to look under the hood and audit AI usage within an enterprise. AI auditability is a crucial step to move past the current inhibitors and into widespread AI adoption—allowing organizations to lay the foundation for trustworthy AI.

Auditability for AI Systems

Auditability and compliance tend to conjure up painful sentiments associated with documentation and governance. However for AI systems, the stakes are much higher. Auditing AI is not much different than auditing any other emerging technology, e.g., cloud computing, cybersecurity, except AI has the potential to disproportionally impact already marginalized groups due to inherent bias in datasets that can be amplified (not to add pressure).

We’ve already seen the current approach play out time and yield perilous impacts on peoples’ lives, inviting a new approach to protect and ensure the integrity of AI-enabled decision-making. Fortunately, by auditing AI systems’ behavior, we can proactively mitigate and prevent some of these negative impacts.

Transparency is key to both end user and stakeholders’ ability to understand and trust AI, and auditability is one component of transparency.

For a data scientist to know that AI is behaving as expected, they must be able to access performance metrics, easily identify when models have deviated from expected results, and have recommendations to be able to course correct. On the same token, key leaders involved in AI governance must also be able to understand how and where AI is being used across the enterprise including answering questions around who did what, when.

Ultimately, monitoring AI systems via solutions that have ModelOps functionality built in provides organizations the ability to do this both in real-time. And, a historical view around AI performance and usage across an enterprise.

Another component of auditability rests on ensuring that AI is trustworthy, reliable, robust, and can be validated and verified by all stakeholders. Today’s ModelOps tools allow insight into model performance in real-time, as well as automated logging and tracking of individual users’ behavior within the system. Gone are the days where data scientists must dig around to reproduce results or show their work.

These tools automate and track this information for AI, with simple dashboards for stakeholders to see performance metrics tailored to what matters most to them. This means that stakeholders, e.g., business leaders, IT leaders, auditors, security leaders, need information in a digestible way that aligns to their unique roles. Regardless of role or the information need, AI auditability provides the means to a holistic understanding of AI usage across an enterprise.

Auditability and Trustworthy AI

While AI auditability is not a novel or even new concept, it has the potential to catapult adoption by enabling transparent, trustworthy AI. Coupled with advances in AI model explainability, auditability offers a window into an organization’s AI health.

Now is the time to create a strong foundation for long term AI success by documenting and monitoring AI usage and performance. Embracing AI audibility will unlock greater value which leads to increased AI adoption with reduced risks.