  Construct theory in social psychology, developed by George Kelly are mental
constructs to predict and anticipate events. Constructs are how humans
interpret, curate, predict and validate data; information. AI today is biased
because it is trained with a narrow construct as defined by the training data
labels. Machine Learning algorithms for facial recognition discriminate against
darker skin colors and in the ground breaking research papers (Buolamwini, Joy
and Timnit Gebru. Gender Shades: Intersectional Accuracy Disparities in
Commercial Gender Classification. FAT (2018), the inclusion of phenotypic
labeling is proposed as a viable solution. In Construct theory, phenotype is
just one of the many subelements that make up the construct of a face. In this
paper, we present 15 main elements of the construct of face, with 50
subelements and tested Google Cloud Vision API and Microsoft Cognitive Services
API using FairFace dataset that currently has data for 7 races, genders and
ages, and we retested against FairFace Plus dataset curated by us. Our results
show exactly where they have gaps for inclusivity. Based on our experiment
results, we propose that validated, inclusive constructs become industry
standards for AI ML models going forward.
