A Survey of Explainability, Interpretability, and Fairness in Artificial Intelligence

Abstract

Artificial Intelligence (AI) is reshaping divergent industries, influencing both operational practices and strategic decision-making. The need for this paper arises from the lack of a systematic approach to developing robust, interdisciplinary AI applications across various sectors. This paper addresses the problem by providing a structured analysis of AI’s impact using MEASUR’s Collateral Analysis (MCA) framework, identifying key gaps in application development and risk management. A structured literature review examines AI applications across industries such as fashion, education, law enforcement, neuroscience, environmental science, and agriculture, alongside applying MCA to evaluate AI system effectiveness. We contribute to integrating insights from multiple sectors and proposing MCA as a structured method to strengthen AI development processes. Major findings reveal that AI significantly enhances creativity, decision-making, operational efficiency, and sustainability when guided by structured frameworks. We recommend practitioners adopt MCA combined with risk analysis from the Spiral software development methodology to improve AI deployment and adaptability. For researchers, we suggest exploring interdisciplinary integration and expanding ethical considerations in AI system design. The broader impact on society includes promoting more sustainable, efficient, and ethical AI practices, fostering innovation, and addressing global challenges. Future research should focus on merging MCA with dynamic risk management techniques and extending sustainable AI frameworks to emerging industries.