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Answering the Question Why: Explainable AI

The statistical branch of Artificial Intelligence has enamored organizations across industries, spurred an immense amount of capital dedicated to its technologies, and entranced numerous media outlets for the past couple of years. All of this attention, however, will ultimately prove unwarranted unless organizations, data scientists, and various vendors can answer one simple question: can they provide Explainable AI?

Although the ability to explain the results of Machine Learning models—and produce consistent results from them—has never been easy, a number of emergent techniques have recently appeared to open the proverbial ‘black box’ rendering these models so difficult to explain.

One of the most useful involves modeling real-world events with the adaptive schema of knowledge graphs and, via Machine Learning, gleaning whether they’re related and how frequently they take place together.

When the knowledge graph environment becomes endowed with an additional temporal dimension that organizations can traverse forwards and backwards with dynamic visualizations, they can understand what actually triggered these events, how one affected others, and the critical aspect of causation necessary for Explainable AI.

Investments in AI may well hinge upon such visual methods for demonstrating causation between events analyzed by Machine Learning.

As Judea Pearl’s renowned The Book of Why affirms, one of the cardinal statistical concepts upon which Machine Learning is based is that correlation isn’t tantamount to causation. Part of the pressing need for Explainable AI today is that in the zeal to operationalize these technologies, many users are mistaking correlation for causation—which is perhaps understandable because aspects of correlation can prove useful for determining causation.

Causation is the foundation of Explainable AI. It enables organizations to understand that when given X, they can predict the likelihood of Y. In aircraft repairs, for example, causation between events might empower organizations to know that when a specific part in an engine fails, there’s a greater probability for having to replace cooling system infrastructure.

The original article can be found here.

Regarding to AI and Causality, AI World Society (AIWS.net) has created a new section on Modern Causal Inference in 2020. This section will be led by Professor Judea Pearl, who is a pioneering figure on AI Causality and the author of the well-known book The Book of Why. Professor Judea’s work will contribute to Causal Inference for AI transparency, which is one of important AIWS topics.