Who Benefits from AI Explanations? Towards Accessible and Interpretable Systems
As AI systems are increasingly deployed to support decision-making in critical domains, explainability has become a means to enhance the understandability of these outputs and enable users to make more informed and conscious choices. However, despite growing interest in the usability of eXplainable AI (XAI), the accessibility of these methods, particularly for users with vision impairments, remains underexplored. This paper investigates accessibility gaps in XAI through a two-pronged approach. First, a literature review of 79 studies reveals that evaluations of XAI techniques rarely include disabled users, with most explanations relying on inherently visual formats. Second, we present a four-part methodological proof of concept that operationalizes inclusive XAI design: (1) categorization of AI systems, (2) persona definition and contextualization, (3) prototype design and implementation, and (4) expert and user assessment of XAI techniques for accessibility. Preliminary findings suggest that simplified explanations are more comprehensible for non-visual users than detailed ones, and that multimodal presentation is required for more equitable interpretability.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Interpretable Machine Learning for Power Systems: Establishing Confidence in SHapley Additive exPlanations
Interpretable Machine Learning (IML) is expected to remove significant barriers for the application of Machine Learning (ML) algorithms in power systems. This letter first seeks to showcase the benefits of SHapley Additi…
Interpretable Machine LearningAugmented Vision-Language Models: A Systematic Review
Recent advances in visual-language machine learning models have demonstrated exceptional ability to use natural language and understand visual scenes by training on large, unstructured datasets. However, this training pa…
Logical ReasoningAn Agentic Approach to Generating XAI-Narratives
Explainable AI (XAI) research has experienced substantial growth in recent years. Existing XAI methods, however, have been criticized for being technical and expert-oriented, motivating the development of more interpreta…
Evaluating Explanation Without Ground Truth in Interpretable Machine Learning
Interpretable Machine Learning (IML) has become increasingly important in many real-world applications, such as autonomous cars and medical diagnosis, where explanations are significantly preferred to help people better …
BIG-bench Machine LearningInterpretable Machine LearningMedical DiagnosisLess is More: Discovering Concise Network Explanations
We introduce Discovering Conceptual Network Explanations (DCNE), a new approach for generating human-comprehensible visual explanations to enhance the interpretability of deep neural image classifiers. Our method automat…
Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)