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Explainable AI: How to Navigate the New World of Data Regulation

The term “Explainable AI (XAI)” is actually as straightforward as it sounds and revolves around understanding and interpreting how machine learning models work. Through XAI you’ll have a better idea about your ML model’s internal processes and be able to explain it to technical and non-technical stakeholders alike.

In a webinar hosted by AI Data & Analytics Network Eugenio Zuccarelli, a Data Science Leader at CVS Health explored not only the regulatory risks posed by not adopting Explainable AI/ML, but also the business opportunities granted when enterprises understand the AI systems they rely upon.

The Model Deployment Process and Explainable AI’s Major Shift 

Eugenio discussed how ML/AI has become fairly popular in standard business processes, but when it comes to explaining how ML models actually work and which metrics determine its predictions, people often utilize the term “black box.” For industries that rely heavily on ML, it’s essential to have insight into how your models are making crucial decisions and be able to provide proper context behind certain decision-making. In the healthcare industry, for example, the predictions and subsequent decisions from a “black box” ML model may be a bit counterintuitive. Without understanding the reasoning behind some of these decisions it can turn into a life-or-death situation. Just making updates to the model would require approval from a governing body like the FDA for major changes or safety approval for minor changes.

Explainable AI, also known as interpretable ML, addresses the challenge of building trust in machine learning models. When organizations use data science and machine learning techniques to extract insights from data, they may encounter resistance when asked to provide details about the models that are making decisions. This is because traditional methods of decision-making rely on human expertise, while machine learning models use complex algorithms to process large amounts of data. Explainable AI provides transparency into the inner workings of these models, making it easier to understand how they are making decisions and how they are impacting the underlying systems. This increased trust in the models leads to more widespread adoption and ultimately, better business value. By using Explainable AI, machine learning models can provide even more value than traditional “black box” models.

Enterprise Tools to Integrate Explainable AI

Achieving a functional XAI system can be complex, but some technologies can help organizations easily reach an explainable model deployment solution sooner. For example, Wallaroo’s Model Insights framework supports model monitoring in an intuitive way allowing you to specify the inputs or outputs that you want to monitor. You can specify how often to monitor distributions and understand the process behind meaningful changes. Likewise, there are similar packages that can help both interpretability and explainability.

In Eugenio’s opinion XAI might bring up concerns regarding data sovereignty, but needless to say, businesses take a lot of risks when they are not adopting an explainable AI system. With explainability, it is easier to interpret the odd behaviors that might happen within models and MLOps solutions driven by those decisions. Not adopting explainable AI is a strategic mistake for businesses leveraging machine learning, so speak with one of our experts to learn how to harness the power of explainability at deployML@wallaroo.ai and schedule a demo. You can also give us a try by joining the Wallaroo community with Wallaroo: Community Edition.

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