Machine learning models are growing in complexity and researchers are improving models that have trouble understanding decisions. This interesting subject came to us from RT Insights in their article, “MIT Researchers Create Explanation Taxonomy to ML Models.”
In finance, healthcare, and logistics, businesses are attempting to implement artificial intelligence (AI) in their decision making processes, but they are finding that their decision makers often reject or doubt AI systems due to difficulties with understanding how the AI came to a certain observation or decision.
Researchers at MIT have been working on a solution to this issue, by building a taxonomy that is inclusive to all types of people who interact with a machine learning model. The taxonomy covers how best to explain and interpret different features, but also how to transform hard-to-understand features into formats that are easier to understand for non-technical users.
The real challenge is that most organizations have little knowledge on how AI systems make decisions. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms.
Melody K. Smith
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