A lot of fear surrounds artificial intelligence (AI). Some is connected to perceived job security and some is related to technology “doomsday” scenarios. The University of Waterloo researchers have developed a new explainable AI model to reduce bias and enhance trust and accuracy in machine learning-generated decision-making and knowledge organization. This interesting information came to us from ProList in their article, “New model reduces bias and enhances trust in AI decision-making and knowledge organization.”

It’s important to note that while some fear is valid to an extent, not all AI technologies pose the same level of risk, and many positive applications of AI can improve various aspects of our lives. Addressing hesitation requires a combination of responsible development, ethical guidelines, regulatory oversight and public education about AI capabilities and limitations.

The biggest challenge most organizations have with fear and hesitancy lies in understanding AI decision-making and in trusting AI and machine learning results. 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.