ANALYZING FAIRNESS AND NON-DISCRIMINATION PRINCIPLES IN THE DESIGN, IMPLEMENTATION, AND EVALUATION OF COMPUTER VISION MACHINE LEARNING DEPLOYMENTS

Abstract

As computer vision machine learning technologies become increasingly deployed in various domains, ensuring fairness and non-discrimination in their design, implementation, and evaluation is of paramount importance. Biased or discriminatory outcomes from these systems can perpetuate social inequalities, violate fundamental rights, and erode public trust in the technology. This research paper explores the principles of fairness and non-discrimination in the context of computer vision machine learning deployments. It examines the sources and manifestations of bias in these systems, the potential consequences of discriminatory outcomes, and the strategies for mitigating bias and promoting fairness. The paper emphasizes the importance of incorporating fairness considerations throughout the entire lifecycle of computer vision machine learning systems, from data collection and model development to deployment and ongoing evaluation. It also highlights the need for diverse stakeholder engagement, transparency, and accountability in the pursuit of fair and non-discriminatory computer vision machine learning deployments. By adhering to principles of fairness and non-discrimination, we can work towards building computer vision machine learning systems that are inclusive, equitable, and beneficial for all members of society.