ClarifAI: Enhancing AI Interpretability and Transparency through Case-Based Reasoning and Ontology-Driven Approach for Improved Decision-Making
This addresses the need for interpretable AI for stakeholders in critical applications, but it appears incremental as it combines existing methods like CBR and ontologies.
The paper tackles the problem of AI interpretability and transparency by introducing ClarifAI, which uses case-based reasoning and an ontology-driven approach to provide explanations for AI decisions, aiming to improve decision-making in high-stake environments.
This Study introduces Clarity and Reasoning Interface for Artificial Intelligence(ClarifAI), a novel approach designed to augment the transparency and interpretability of artificial intelligence (AI) in the realm of improved decision making. Leveraging the Case-Based Reasoning (CBR) methodology and integrating an ontology-driven approach, ClarifAI aims to meet the intricate explanatory demands of various stakeholders involved in AI-powered applications. The paper elaborates on ClarifAI's theoretical foundations, combining CBR and ontologies to furnish exhaustive explanation mechanisms. It further elaborates on the design principles and architectural blueprint, highlighting ClarifAI's potential to enhance AI interpretability across different sectors and its applicability in high-stake environments. This research delineates the significant role of ClariAI in advancing the interpretability of AI systems, paving the way for its deployment in critical decision-making processes.