Every day, the news highlights how emerging technologies like artificial intelligence (AI) and machine learning are changing businesses operations, improving education, and impacting personal and professional quality of life. This news from Acceleration Economy should therefore come as no surprise: “How AI and Machine Learning (ML) Help Prevent Sports Injuries.”

Competitive sports have long been using AI, machine learning, and data science to choose players and decide lineups, but now they’re able to predict and prevent injuries by examining large amounts of biometric data gathered by sensors placed directly on athletes as they train.

Data-based prediction analysis is being used by soccer and football leagues for injury prediction. The data helps coaches adjust players’ training schedules and playing time.

Understanding AI and machine learning algorithms’ decision-making and output is important. 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.

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