Evaluating Transparency and Explainability in AI-Driven Planning and Scheduling: A Comprehensive Literature Review

• Published in: International journal of innovative research in science, engineering and technology Vol. 12; no. 6; pp. 9008 - 9014
• Main Authors: Thakre, Prof. Neha, Sahu, Prof. Ranu, Soni, Prof. Kuldeep, Bisen, Deepshika, Sahu, Anshika
• Format: Journal Article
• Language: English
• Published: 25.11.2023
• ISSN: 2347-6710; 2319-8753
• DOI: 10.15680/IJIRSET.2023.1206155

As AI technologies rapidly integrate into our work environments, the need for systems that effectively collaborate with humans grows. A crucial aspect of this interaction is the explainability of AI behavior to human users. While AI has surpassed human decision-making abilities in many areas, its opaque algorithms often produce accurate yet incomprehensible results. This lack of transparency can hinder trust between humans and machines. The TUPLES project, a three-year Horizon Europe research initiative, seeks to address this by developing AI-based planning and scheduling tools with a human-centered focus. By utilizing both data-driven and symbolic AI methods, TUPLES aims to create scalable, transparent, robust, and secure systems. The project employs a use-case-oriented approach, drawing from industry expertise to ensure relevance in areas such as manufacturing, aviation, and waste management. The EU guidelines for Trustworthy AI emphasize key principles like human oversight, transparency, fairness, societal well-being, and accountability. The Assessment List for Trustworthy Artificial Intelligence (ALTAI) serves as a selfassessment tool for evaluating AI systems. Current planning and scheduling tools only partially meet these criteria, highlighting the need for innovative AI development. Our literature review explored the transparency and explainability of AI algorithms in planning and scheduling, identifying metrics and recommendations. The study underscores the role of Explainable AI (XAI) in addressing the black box problem, using techniques that make AI systems more understandable and accurate. XAI relies on human explanation concepts to ensure a human-centered approach, offering specific methods and guidance for selecting appropriate tools in planning and scheduling contexts.