How to choose an Explainability Method? Towards a Methodical Implementation of XAI in Practice
Explainability is becoming an important requirement for organizations that make use of automated decision-making due to regulatory initiatives and a shift in public awareness. Various and significantly different algorithmic methods to provide this explainability have been introduced in the field, but the existing literature in the machine learning community has paid little attention to the stakeholder whose needs are rather studied in the human-computer interface community. Therefore, organizations that want or need to provide this explainability are confronted with the selection of an appropriate method for their use case. In this paper, we argue there is a need for a methodology to bridge the gap between stakeholder needs and explanation methods. We present our ongoing work on creating this methodology to help data scientists in the process of providing explainability to stakeholders. In particular, our contributions include documents used to characterize XAI methods and user requirements (shown in Appendix), which our methodology builds upon.
Code (0)
등록된 구현이 없습니다.
Tasks
Decision MakingExplainable Artificial Intelligence (XAI)Similar Papers 제목 키워드 기반
Out-of-Distribution Detection for Medical Applications: Guidelines for Practical Evaluation
Detection of Out-of-Distribution (OOD) samples in real time is a crucial safety check for deployment of machine learning models in the medical field. Despite a growing number of uncertainty quantification techniques, the…
BIG-bench Machine LearningOut-of-Distribution DetectionOut of Distribution (OOD) DetectionUncertainty QuantificationSolving ImageNet: a Unified Scheme for Training any Backbone to Top Results
ImageNet serves as the primary dataset for evaluating the quality of computer-vision models. The common practice today is training each architecture with a tailor-made scheme, designed and tuned by an expert. In this pap…
Image ClassificationKnowledge DistillationDOLOMITES: Domain-Specific Long-Form Methodical Tasks
Experts in various fields routinely perform methodical writing tasks to plan, organize, and report their work. From a clinician writing a differential diagnosis for a patient, to a teacher writing a lesson plan for stude…
FormA Comparative Study on Automatic Coding of Medical Letters with Explainability
This study aims to explore the implementation of Natural Language Processing (NLP) and machine learning (ML) techniques to automate the coding of medical letters with visualised explainability and light-weighted local co…
The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategies
Artificial intelligence (AI) has huge potential to improve the health and well-being of people, but adoption in clinical practice is still limited. Lack of transparency is identified as one of the main barriers to implem…