  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 the 2021 Google Landmark Challenges, which was the first
of a kind with an emphasis on fairness in generic object recognition.
