IBM Addresses AI Bias with Massive Image Archive

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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."

Director at IBM Research Arvind Krishna opened IBM’s Think 2018 conference in Las Vegas this week by introducing the firm’s ‘5 in 5’ presentation. Delivered as an almost TED Talk style.

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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.

We at IBM have created a solution specifically to address AI bias.. the launch of AI Fairness 360, an open-source library to help detect and remove. virtual and augmented reality, automated investing, improved medical imaging, and more.. Huge volumes of collected data present storage and analytics.

Big Blue’s software. or sometimes the unconscious bias in some developers who may have programmed the initial instructions for the AI forgetting to address certain attributes of say a race that’s.

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.

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IBM Prediction: ‘AI Bias’ to ‘Explode,’ But Only ‘Unbiased AI Will Survive’ Story Highlights As artificial intelligence (AI) continues to proliferate, the amount of "biased" AI systems and algorithms will significantly rise, but only "unbiased" AI will survive, according to Arvind Krishna, head of IBM Research.

Mitigating Bias in AI Models.. IBM Research gathered images of parliamentarians from Finland, Iceland, Rwanda, Senegal, South Africa and Sweden. This dataset is very similar to the Pilot Parliaments Benchmark.. but current synthesis approaches only address the scenario of a single label.

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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.