What Does Explainable AI Really Mean? A New Conceptualization of Perspectives

Abstract

We characterize three notions of explainable AI that cut across research fields: opaque systems that offer no insight into its algo- rithmic mechanisms; interpretable systems where users can mathemat- ically analyze its algorithmic mechanisms; and comprehensible systems that emit symbols enabling user-driven explanations of how a conclusion is reached. The paper is motivated by a corpus analysis of NIPS, ACL, COGSCI, and ICCV/ECCV paper titles showing differences in how work on explainable AI is positioned in various fields. We close by introducing a fourth notion: truly explainable systems, where automated reasoning is central to output crafted explanations without requiring human post processing as final step of the generative process.

Cited by in corpus (23)

• A Survey on the Explainability of Supervised Machine Learning
• What do we need to build explainable AI systems for the medical domain?
• What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research
• Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey
• Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI
• Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey
• To trust or not to trust an explanation: using LEAF to evaluate local linear XAI methods
• Explainable Artificial Intelligence (XAI): An Engineering Perspective
• Towards Explainable Neural-Symbolic Visual Reasoning
• Explainable Machine Learning with Prior Knowledge: An Overview
• User-centered Feedback Design in Person-following Robots for Older Adults
• Generating User-friendly Explanations for Loan Denials using GANs
• Relating Input Concepts to Convolutional Neural Network Decisions
• Collaboratively boosting data-driven deep learning and knowledge-guided ontological reasoning for semantic segmentation of remote sensing imagery
• Sequential Explanations with Mental Model-Based Policies
• Quantifying the Task-Specific Information in Text-Based Classifications
• A Gaze into the Internal Logic of Graph Neural Networks, with Logic
• Ten AI Stepping Stones for Cybersecurity
• Explainable AI for Classification using Probabilistic Logic Inference
• Teaching Solid Mechanics to Artificial Intelligence: a fast solver for heterogeneous solids
• Generalized Constraints as A New Mathematical Problem in Artificial Intelligence: A Review and Perspective
• Scientific Calculator for Designing Trojan Detectors in Neural Networks
• Memory networks for consumer protection:unfairness exposed