ASSESSING INFORMED CONSENT PROCEDURES AND ETHICAL DATA UTILIZATION FRAMEWORKS IN MACHINE LEARNING RESEARCH AND APPLICATIONS

Abstract

The proliferation of computer vision machine learning technologies across diverse fields necessitates a critical examination of ethical challenges surrounding informed consent and data usage. These systems frequently rely on the collection, processing, and analysis of sensitive personal data, including facial imagery and biometric identifiers, underscoring the importance of ensuring individuals are adequately informed and voluntarily consent to participation. This study investigates the existing practices and protocols governing informed consent and ethical data utilization in computer vision research and applications. It addresses the difficulties in securing meaningful consent, the risks and consequences of data misuse, and methods to enhance transparency, accountability, and respect for individual autonomy. The analysis highlights the imperative for comprehensive ethical frameworks and guidelines that safeguard privacy, dignity, and individual rights while facilitating the responsible evolution of computer vision technologies. By critically assessing and refining these practices, this paper advocates for fostering public trust, reducing ethical vulnerabilities, and promoting the socially responsible advancement of computer vision machine learning systems.