IBM Addresses AI Bias with Massive Image Archive

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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According to IBM, the current largest facial attribute dataset is made up of just 200,000 images. IBM will also be releasing an annotated dataset of up to 36,000 images that are equally distributed across skin tones, genders, and ages. The company hopes that it will help algorithm designers to identify and address bias in their facial analysis.

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AI bias will explode. But only the unbiased AI will survive. Within five years, the number of biased AI systems and algorithms will increase. But we will deal with them accordingly – coming up with new solutions to control bias in AI and champion AI systems free of it.

When looking across all the different industries, few, like the Electronics industry, can claim to be at the core of most disruptions and be the object of major disruptions itself. Using Data By.

IBM has unveiled its latest offering, the fairness 360 kit, which will help identify any bias in AI decision making and recommend adjustments. It’s area of the burgeoning artificial intelligence segment which could prove to be its downfall. AI is supposed to set of technologies designed to make.

eWeek: IBM Addresses AI Bias with Massive Image Archive. "IBM revealed that it will soon make available to the global research community a dataset of 1 million images to improve facial analysis system training; plus a dataset of 36,000 facial images that algorithm designers can use to evaluate bias in their own facial analysis systems."

IBM Addresses AI Bias with Massive Image Archive. It’s also an important factor in how accurate information from a query using artificial intelligence turns out. IBM has built its reputation.

While other open-source resources have focused solely on checking for bias in training data, the IBM AI fairness 360 toolkit created by IBM Research will help check for and mitigate bias in AI models. It invites the global open source community to work together to advance the science and make it easier to address bias in AI.