  Developing robust and fair AI systems require datasets with comprehensive set
of labels that can help ensure the validity and legitimacy of relevant
measurements. Recent efforts, therefore, focus on collecting person-related
datasets that have carefully selected labels, including sensitive
characteristics, and consent forms in place to use those attributes for model
testing and development. Responsible data collection involves several stages,
including but not limited to determining use-case scenarios, selecting
categories (annotations) such that the data are fit for the purpose of
measuring algorithmic bias for subgroups and most importantly ensure that the
selected categories/subcategories are robust to regional diversities and
inclusive of as many subgroups as possible.
  Meta, in a continuation of our efforts to measure AI algorithmic bias and
robustness
(https://ai.facebook.com/blog/shedding-light-on-fairness-in-ai-with-a-new-data-set),
is working on collecting a large consent-driven dataset with a comprehensive
list of categories. This paper describes our proposed design of such categories
and subcategories for Casual Conversations v2.
