It has never been more important for technology to be accessible and approachable. When it comes to artificial intelligence (AI), explainability is key. MIT Management brought this interesting information to our attention in their article, “Why companies need artificial intelligence explainability.”

Creating successful AI programs doesn’t always end with building the right AI system. These programs also need to be integrated into an organization, and stakeholders need to trust that the AI program is accurate and trustworthy.

New machine learning systems will have the ability to explain their rationale, characterize their strengths and weaknesses and convey an understanding of how they will behave in the future. The strategy for achieving that goal is to develop new or modified machine learning techniques that will produce more explainable models.

It has never been more important for organizations to have knowledge of how AI systems make the decisions they do, and how the results are applied in various fields. 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 potential biases.

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.