As another example, a research team requested a review of a new ML training dataset for computer vision fairness techniques that offered more specific attributes about people, such as perceived gender and age-range. The team worked with Open Images, an open data project containing ~9 million images spanning thousands of object categories and bounding box annotations for 600 classes. Reviewers weighed the risk of labeling the data with the sensitive labels of perceived gender presentation and age-range, and the societal benefit of these labels for fairness analysis and bias mitigation. Given these risks, reviewers required creation of a data card explaining the details and limitations of the project. We released the MIAP (More Inclusive Annotations for People) dataset in the Open Images Extended collection. The collection contains more complete bounding box annotations for the person class hierarchy in 100K images containing people. Each annotation is also labeled with fairness-related attributes. MIAP was accepted and presented at the 2021 Artificial Intelligence, Ethics and Society conference.

Source: Blog.google

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