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 to operate 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, more precisely, machine learning feed on this data: the more of it we have, the more we are able to understand why it looks the way it does and how it interconnects.
Today, to mitigate risks, we are 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 have little knowledge on 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. |
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Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.
