Researchers are striving to improve the interpretability of features so decision makers will be more comfortable using the outputs of machine learning models. They have developed a taxonomy to help developers craft features that will be easier for their target audience to understand. This interesting information came to us from Science Daily in their article, “Building explainability into the components of machine-learning models.”
Explanation methods that help users understand and trust machine learning models often describe how much certain features used in the model contribute to its predictiveness. For example, if a model predicts a patient’s risk of developing cardiac disease, a physician might want to know how strongly the patient’s heart rate data influences that prediction.
Most organizations have little knowledge on how artificial intelligence (AI) systems make decisions. As a result, the application of findings from AI and machine learning models are difficult for organizations to understand. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms. Explainable AI is used to describe an AI model, its expected impact, and its potential biases. Why is this important? Because explainability becomes critical when the results can have an impact on data security or safety.
Melody K. Smith
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