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Racial Bias in Computer Vision via Convolutional Neural Networks
  • Kunal Srivastava ,
  • Sean Lim ,
  • Connor Chan
Kunal Srivastava
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Connor Chan
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Abstract

We analyzed the widely popular UTKFace dataset to implement a Convolutional Neural Network (CNN) algorithm, used to predict gender given an image of the human. Over the course of the experiment, we observed various statistics of the dataset, to give a strong representation of what could drive the results our model returned. By further investigating the current policy as it comes to data collection and privacy, we hope to suggest practices that will lead humanity to a safer, more equal future. Through the analysis of images from popular datasets, optimization of machine learning models, and the changing information industry standards, we aim to find and assess a difference in accuracies of gender-prediction models when it comes to race.