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IBM plans to release more than 1 million facial images to help better train the artificial intelligence behind facial recognition systems. Why it matters: The risk of bias being built into AI systems is a major hurdle for all companies developing facial analysis algorithms to, for example, recognize different skin colors and other attributes in a non-discriminatory way.
Last but not least, the IBM AI Fairness 360 toolkit addresses the most urgent need for enterprise AI, model bias. The AI Fairness 360 open source toolkit not only automatically detects bias but also quantifies it. The toolkit provides a set of algorithms to mitigate that bias to remove it and will be a part of every ML and AI DevOps pipeline.
A growing number of companies see AI as critical to their future. But concerns about possible misuse of the technology are on the rise. In a recent Deloitte survey, 76 percent of executives said they expected AI to "substantially transform" their companies within three years, 7 while about a third of respondents named ethical risks as one of the top three concerns about the technology.
Many other researchers have shown how AI systems, which look for patterns in huge troves of data. “urgent attention” to address the bias. “I responded pretty much right away,” said Ruchir Puri,
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Now that AI is a reality, conversations about how to responsibly use AI in our lives is finally taking place, and bias is a chief concern for the future of AI as a helpful part of our lives including recruiting. Here are some highlights from IBM’s Think 2018 Conference on the subject.
AI is a reflection of its creator. To understand how bias is becoming embedded into AI, we have to look at how we build it. To start, AI creators design these experiences by forming the questions.
A key to any effective data management solution is to address the entire data lifecycle. Data managers must be able to archive. and manage massive amounts of data more efficiently and securely,
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Many other researchers have shown how AI systems, which look for patterns in huge troves of data. “urgent attention” to address the bias. “I responded pretty much right away,” said Ruchir Puri,