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

Today companies are demanding, collecting, and working on more data than ever before. Emerging technologies like AI and machine learning are dependent on this data. The rule is: the more data, the better. With more of it, we are better able to understand both the data and its sources.

AI can also be used to minimize the risk of privacy breaches by encrypting personal data, reducing human error, and detecting potential cybersecurity incidents.

The biggest challenge is understanding. Most organizations have little understanding of AI decision making, and as a result, they know little about how AI results can be utilized for cybersecurity, compliance, and profit. 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.