Auditing Framework

Auditing frameworks are being developed to systematically evaluate the fairness, accuracy, and trustworthiness of increasingly prevalent AI systems, particularly in high-stakes applications like hiring and chatbot development. Current research focuses on establishing standardized metrics and methodologies for assessing various aspects of AI, including bias detection, explanation verification, and the impact of different levels of access to the underlying algorithms (e.g., black-box vs. white-box). These frameworks aim to improve accountability and transparency in AI, fostering trust and mitigating potential harms by providing a structured approach to identifying and addressing issues before deployment and during operation.