  A diverse representation of different demographic groups in AI training data
sets is important in ensuring that the models will work for a large range of
users. To this end, recent efforts in AI fairness and inclusion have advocated
for creating AI data sets that are well-balanced across race, gender,
socioeconomic status, and disability status. In this paper, we contribute to
this line of work by focusing on the representation of age by asking whether
older adults are represented proportionally to the population at large in AI
data sets. We examine publicly-available information about 92 face data sets to
understand how they codify age as a case study to investigate how the subjects'
ages are recorded and whether older generations are represented. We find that
older adults are very under-represented; five data sets in the study that
explicitly documented the closed age intervals of their subjects included older
adults (defined as older than 65 years), while only one included oldest-old
adults (defined as older than 85 years). Additionally, we find that only 24 of
the data sets include any age-related information in their documentation or
metadata, and that there is no consistent method followed across these data
sets to collect and record the subjects' ages. We recognize the unique
difficulties in creating representative data sets in terms of age, but raise it
as an important dimension that researchers and engineers interested in
inclusive AI should consider.
