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Better Transparency and Fairness in AI Systems

Posted July 28, 2020 in Data Analytics & Digital Technologies
Better Transparency and Fairness in AI Systems
Neural-symbolic artificial intelligence (AI) combines the pattern-recognition and pattern-matching capabilities of neural networks with the symbolic-reasoning functionality and transparency features of rule-based and knowledge-based systems (KBSs). One of the main goals of neural-symbolic AI is the development of hybrid AI systems that are more explainable and transparent in their reasoning.
About The Author
Curt Hall
Curt Hall is a Senior Consultant with Cutter Consortium’s Data Analytics & Digital Technologies and Business & Enterprise Architecture practices. He has extensive experience as an IT analyst covering technology trends, application development trends, markets, software, and services. Mr. Hall's expertise includes artificial intelligence (AI), cognitive systems, machine learning (ML), conversational computing, and advanced analytics. He… Read More
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