IBM Addresses AI Bias with Massive Image Archive

Mitigating Bias in AI Models. February 6, 2018 | Written by:. IBM Research gathered images of parliamentarians from Finland, Iceland, Rwanda, Senegal, South Africa and Sweden.. we have several ongoing projects to address dataset bias in facial analysis – including not only gender and.

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.

According to an IBM press statement, "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.

IBM adding recommended bias monitors to Watson OpenScale. These recommended bias monitors automatically identify attributes like sex, ethnicity, marital status and age and recommend they be monitored.

IBM Addresses AI Bias with Massive Image ArchiveIBM 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.

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Garry kasparov playing. bias,” the unintended discriminatory outcomes of some machine-learning algorithms. His emphasis on AI as a campaign issue may set him apart, but his proposals are generally.

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.

IBM Research has been exploring artificial intelligence and machine learning technologies and techniques for decades. We believe AI will transform the world in dramatic ways in the coming years – and we’re advancing the field through our portfolio of research focused on three areas: towards human-level intelligence, platform for business, and hardware and the physics of AI.

For the task of determining the gender of a face, we used the same methodology that revealed substantial gender and skin type bias in AI services sold by Microsoft, IBM, and (Megvii) Face++ to.