Artificial intelligence (AI) appears to be on the brink of remaking both the economy and politics, and perhaps to a far greater extent than anyone expects. It has been predicted that AI may soon be able to cure diseases such as cancer and mitigate, if not reverse, the effects of climate change. This, they explain, will be the first human invention capable of inventing things we could never invent for ourselves—things that promise to make our lives longer, easier, and more enjoyable. All we have to do is get out of its way. This interesting information came to us from Commonweal in their article, “Hitting Pause on Artificial Intelligence.“
Is it understood well enough, however, for people to use it optimally? Explainable AI is a subset of AI that focuses on creating algorithms and models that can be easily understood by humans. The goal of explainable AI is to increase transparency and trust in AI systems, by providing explanations for the decisions its makes. As AI systems are integrated into various industries, including healthcare, finance, and transportation, understanding becomes increasingly more important..
In these fields, it is crucial for decision-makers to understand how AI systems arrive at their conclusions and recommendations. There are several approaches to achieving explainability in AI systems. One approach involves using transparent models such as decision trees or rule-based systems. These models can be easily interpreted by humans, but they may not always provide the best accuracy. Another approach involves using black box models such as deep neural networks but incorporating techniques that allow for explanations of their output.
By developing transparent and interpretable AI systems, we can increase trust in these technologies while also improving their accuracy and performance.
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Melody K. Smith
Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.
