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Improving Fairness in Large-Scale Object Recognition by CrowdSourced Demographic Information

Bingyi Cao
Cam Askew
Jack Sim
Mike Green
N'Mah Fodiatu Yilla-Akbari
Zu Kim
arXiv (2022)
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There has been increasing awareness of ethical issues in machine learning, and fairness has become an important research topic. Most fairness efforts in computer vision have been focused on human sensing applications and preventing discrimination by people's physical attributes such as race, skin color or age by increasing visual representation for particular demographic groups. We argue that ML fairness efforts should extend to object recognition as well. Buildings, artwork, food and clothing are examples of the objects that define human culture. Representing these objects fairly in machine learning datasets will lead to models that are less biased towards a particular culture and more inclusive of different traditions and values. There exist many research datasets for object recognition, but they have not carefully considered which classes should be included, or how much training data should be collected per class. To address this, we propose a simple and general approach, based on crowdsourcing the demographic composition of the contributors: we define fair relevance scores, estimate them, and assign them to each class. We showcase its application to the landmark recognition domain, presenting a detailed analysis and the final fairer landmark rankings. We present analysis which leads to a much fairer coverage of the world compared to existing datasets. The evaluation dataset was used for a public image recognition challenge, which was the first of a kind with an emphasis on fairness in generic object recognition.