Investigating the Significance of Transparency and Explainability in Computer Vision Machine Learning Models for Ethical Decision Making

Abstract

As computer vision machine learning models become increasingly prevalent in various domains, ranging from healthcare and finance to criminal justice and autonomous vehicles, the need for transparency and explainability in these models has become paramount. The opaque nature of many machine learning algorithms raises concerns about their fairness, accountability, and potential for bias, which can have significant ethical implications. This research paper explores the importance of transparency and explainability in computer vision machine learning models, particularly in the context of ethical decision making. It examines the challenges associated with achieving transparency and explainability, the current approaches and techniques used to address these challenges, and the benefits of transparent and explainable models for fostering trust, ensuring fairness, and enabling accountability. The paper also discusses the ethical considerations surrounding the use of computer vision machine learning models and highlights the need for a multi-stakeholder approach to developing and deploying these models responsibly. By promoting transparency and explainability, we can work towards building more ethical and trustworthy computer vision machine learning models that align with societal values and promote the well-being of individuals and communities.