Bridging the Gap between Theory and Practice: Towards Responsible AI Evaluation

The growing integration of artificial intelligence (AI) in diverse sectors underscores the need for comprehensive and standardized approaches to ensure AI responsibility. However, the absence of a holistic framework to evaluate the fairness, privacy-preserving, secure, explainable, and human-centered facets of AI systems poses a challenge. Addressing this gap, this research paper presents a novel approach to assessing Responsible AI by combining insights from a systematic literature review with a practical evaluation framework. The paper provides a concise overview of the key aspects of Responsible AI and highlights the findings from the literature review. Furthermore, the paper introduces a set of evaluation metrics specifically designed for the current state of the art, using different model types and data from the healthcare domain. The framework supports the evaluation of Natural Language Processing (NLP), Computer Vision (CV), and tabular data models for classification tasks. Additionally, the paper briefly demonstrates VERIFAI, an example implementation of the framework, which serves as a comprehensive tool for assessing the responsibility of AI systems. The overall objective of this research is to make a meaningful contribution to the Responsible AI discourse, providing researchers and practitioners with a valuable resource to enhance the overall responsibility of their AI systems.