IBM Addresses AI Bias with Massive Image Archive

A dataset of annotations for over 1 million images to improve the understanding of bias in facial analysis being built by IBM Research scientists. Images will be annotated with attributes, leveraging geo-tags from Flickr images to balance data from multiple countries and active learning tools to reduce sample selection bias.

Meanwhile, professors at Harvard Law School are working on ways to eliminate unfair bias. AI system allows students to ask questions via text message, and get answers-generated through so-called.

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AI is. These images are just a tiny selection. Thankfully, there’s a growing number of people researching and writing about this. I really strongly recommend you check out propublica.org, who do.

help detect and mitigate algorithmic bias and to get explanations for AI outputs. But, she says, that was just the start. ibm watson openscale has now been augmented to make it easier to detect and.

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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.

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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.

Many tech entrepreneurs, alongside the big players Alphabet’s Verily, Google’s DeepMind and IBM Watson. into Sophia DDM platform, the AI transforms gene data into digital information and ejects.

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.

IBM is trying combat this bias with a new dataset that offers a better balance of diverse faces.. Bias is a big problem in facial. The first dataset contains 1 million images and will help.

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