The Impact of Biases in Facial Recognition Artificial Neural Networks

Abstract

This study probes how biases are formed, and then mitigated within artificial neural networks for facial recognition. In current research on facial recognition neural networks, it has been shown that there are many ways that biases/prejudices can negatively affect the accuracy of the network on characteristics such as gender status and identity. In order to test this, two pre-trained neural networks were fed novel datasets - one on cisgender faces and one on transgender faces. The two pre-trained models were then analyzed with regards to gender identity and status variables on accuracy rates calculated from the direct prediction outputs provided by the neural networks. Notable biases were found within both datasets and models on gender characteristics.

Type
Publication
Illuminate, 5, 25-32.

Due to the issues of AI Ethics detailed within this publication, the datasets used for testing purposes are not able to be shared to preserve the identities of all individuals within the transgender and cisgender groups.

For other information about my Honors Thesis, feel free to search through the AI Bias project. Other information detailed within the project section include conference presentations and pertinent background information about the need for such research.

Ezra Wingard
Ezra Wingard
Gradute of Cognitive Science and Psychology, Programmer, and AI Enthusiast

Aspiring researcher and data analyst with a passion for an array of subjects including data analysis, language acquisition, and cognition/memory.