Frameworks for Accountability and Responsibility Among Stakeholders in Computer Vision Machine Learning Development and Deployment

Abstract

The rapid advancement and widespread adoption of computer vision machine learning technologies have raised important questions about accountability and responsibility in their development and deployment. As these systems become increasingly integrated into various domains, from healthcare and finance to public safety and autonomous vehicles, it is crucial to establish clear frameworks that define the roles, obligations, and liabilities of the diverse stakeholders involved. This research paper examines the existing accountability and responsibility frameworks for stakeholders in the computer vision machine learning ecosystem, including developers, deployers, users, and regulators. It explores the challenges associated with assigning accountability in complex, multi-stakeholder environments and discusses the potential consequences of system failures or unintended outcomes. The paper also highlights the need for proactive governance mechanisms, such as ethical guidelines, standards, and regulations, to ensure the responsible development and deployment of computer vision machine learning systems. By establishing robust accountability and responsibility frameworks, we can foster trust, mitigate risks, and promote the ethical and beneficial use of these technologies for society as a whole.