Equilibrating Efficiency, Accuracy, and Ethical Considerations in the Development and Deployment of Computer Vision Machine Learning Solutions
Hasini Dilani Ranasinghe
Department of Environmental Science, University of Sri Jayewardenepura, Nugegoda 10250, Sri Lanka
Abstract
The rapid advancements in computer vision and machine learning technologies have led to the development of sophisticated solutions that offer significant benefits in terms of efficiency and accuracy. These solutions have the potential to revolutionize various domains, from healthcare and transportation to security and manufacturing. However, the pursuit of efficiency and accuracy in computer vision machine learning solutions must be balanced with important ethical considerations. This research paper explores the challenges and trade-offs involved in balancing efficiency, accuracy, and ethical concerns in the development and implementation of computer vision machine learning solutions. It examines the potential risks and unintended consequences of prioritizing efficiency and accuracy over ethical considerations, such as privacy violations, bias and discrimination, and the erosion of human agency. The paper also discusses strategies for achieving a responsible balance, including the incorporation of ethical principles into the design and development process, the use of diverse and representative datasets, and the implementation of transparency and accountability measures. By critically analyzing these issues and proposing actionable recommendations, this paper aims to contribute to the responsible and ethical advancement of computer vision machine learning technologies.
Author Biography
Hasini Dilani Ranasinghe, Department of Environmental Science, University of Sri Jayewardenepura, Nugegoda 10250, Sri Lanka
