# Introducing Construct Theory as a Standard Methodology for Inclusive AI Models

arXiv: https://arxiv.org/abs/2304.09867 · HTML: https://mlsanity.com/paper/2304.09867/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 19 references that are in the map.
- First posted on arXiv 19 Apr 2023.

## Builds on (most cited first, 3 of 3)

- [A Deeper Look at Dataset Bias](https://mlsanity.com/paper/1505.01257/) (2015)
- [Biased Programmers? Or Biased Data? A Field Experiment in Operationalizing AI Ethics](https://mlsanity.com/paper/2012.02394/) (2020)
- [Cognitive Biases in Software Engineering: A Systematic Mapping Study](https://mlsanity.com/paper/1707.03869/) (2017)

## Abstract (arXiv)

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.