It is understood that artificial intelligence (AI) models that are built on consumer data must also be built with data privacy in mind. Some users are hesitant of automated systems that collect and use their data, so to remain viable, AI models must incorporate privacy protection into their design. This interesting information came to us from Technology in their article, “AI and data privacy: protecting information in a new era.”

Companies are demanding, collecting, and working on more data than ever before. AI and machine learning depend on that data: the more data is available, the better informed the possible decisions and the better understanding of behaviors and interconnections.

For the mitigation of risks, the market is seeing attempts to regulate AI both in industry and government and build a foundation of trustworthiness that can keep the fictitious stories on the side of fiction.

The real problem is that most organizations know little about how AI systems make decisions, and as a result, they don’t know how the results apply to their bottom lines. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms.

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.