Researchers are striving to improve their descriptions 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 audiences to understand. This interesting information came to us from Science Daily in their article, “Building explainability into the components of machine-learning models.”

Descriptions helping users understand and trust machine learning often point out features contributing to its predictive modelling ability. 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 regarding artificial intelligence (AI) systems’ decisions-making ability and, as a result, how to apply AI in their business operations. 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

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.