Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Direction

Abstract

Artificial intelligence models encounter significant challenges due to their black-box nature, particularly in safety-critical domains such as healthcare, finance, and autonomous vehicles. Explainable Artificial Intelligence (XAI) addresses these challenges by providing explanations for how these models make decisions and predictions, ensuring transparency, accountability, and fairness. Existing studies have examined the fundamental concepts of XAI, its general principles, and the scope of XAI techniques. However, there remains a gap in the literature as there are no comprehensive reviews that delve into the detailed mathematical representations, design methodologies of XAI models, and other associated aspects. This paper provides a comprehensive literature review encompassing common terminologies and definitions, the need for XAI, beneficiaries of XAI, a taxonomy of XAI methods, and the application of XAI methods in different application areas. The survey is aimed at XAI researchers, XAI practitioners, AI model developers, and XAI beneficiaries who are interested in enhancing the trustworthiness, transparency, accountability, and fairness of their AI models.

References in corpus (33)

• Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
• Towards A Rigorous Science of Interpretable Machine Learning
• Methods for Interpreting and Understanding Deep Neural Networks
• Striving for Simplicity: The All Convolutional Net
• Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models
• A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT
• Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making
• What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research
• Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions
• Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey
• Attention is not Explanation
• Interpretable and Explainable Machine Learning for Materials Science and Chemistry
• What Does Explainable AI Really Mean? A New Conceptualization of Perspectives
• A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?
• Explainable Deep Reinforcement Learning: State of the Art and Challenges
• Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond
• Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey
• Larger language models do in-context learning differently
• NExT-GPT: Any-to-Any Multimodal LLM
• Benchmarking Deep Learning Interpretability in Time Series Predictions
• Towards a Science of Human-AI Decision Making: A Survey of Empirical Studies
• Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions
• A Systematic Review of Robustness in Deep Learning for Computer Vision: Mind the gap?
• Measuring Faithfulness in Chain-of-Thought Reasoning
• How Interpretable are Reasoning Explanations from Prompting Large Language Models?
• Text and Patterns: For Effective Chain of Thought, It Takes Two to Tango
• Pose2Trajectory: Using Transformers on Body Pose to Predict Tennis Player's Trajectory
• On Explaining Your Explanations of BERT: An Empirical Study with Sequence Classification
• Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networks
• Model Explainability in Deep Learning Based Natural Language Processing
• Analyzing Chain-of-Thought Prompting in Large Language Models via Gradient-based Feature Attributions
• Towards Understanding In-Context Learning with Contrastive Demonstrations and Saliency Maps
• Towards Safe, Explainable, and Regulated Autonomous Driving

Cited by in corpus (7)

• Evaluating the Effectiveness of XAI Techniques for Encoder-Based Language Models
• Explainable AI: XAI-Guided Context-Aware Data Augmentation
• Enhancing Trust Through Standards: A Comparative Risk-Impact Framework for Aligning ISO AI Standards with Global Ethical and Regulatory Contexts
• Audio-visual cross-modality knowledge transfer for machine learning-based in-situ monitoring in laser additive manufacturing
• Toxicity in Online Platforms and AI Systems: A Survey of Needs, Challenges, Mitigations, and Future Directions
• Explainable AI: Context-Aware Layer-Wise Integrated Gradients for Explaining Transformer Models
• Feature Attribution in 5G Intrusion Detection: A Statistical vs. Logic-Based Comparison