Predictive AI and Explainable AI: Understanding the Difference

Predictive artificial intelligence (AI) and explainable AI serve different but complementary purposes. Where one focuses on forecasting outcomes, the other helps people understand how an AI system gets to its conclusions. Tech eXplore brought this topic to us in their article, “Explainable AI could help turn hidden data patterns into testable scientific hypotheses.”

Predictive AI analyzes historical data to identify patterns and estimate what is likely to happen next. Organizations use it to forecast customer behavior, assess financial risk, detect fraud and recommend products or content. Its value lies in recognizing trends within large amounts of data more quickly and consistently than people typically can.

An accurate prediction does not always reveal the reasoning behind it. A model may identify a customer as likely to cancel a service, for example, without making the contributing factors immediately clear.

Explainable AI is designed to make that reasoning and process more transparent. It can identify data points or patterns that influenced a result. It allows the system to show its work – familiar to anyone who has ever taken a math class.

It is important that organizations be able to evaluate results, meet regulatory requirements and explain decisions to those most affected by them.

Predictive AI helps organizations anticipate possible outcomes. Explainable AI provides insight into how those outcomes were identified. Used together, they support decisions that are not only data-informed but also more transparent and trustworthy.

Melody K. Smith

Data Harmony is an award-winning semantic suite that leverages explainable AI.

Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.