Evaluating the Quality of Machine Learning Explanations: A Survey on Methods and Metrics
The most successful Machine Learning (ML) systems remain complex black boxes to end-users, and even experts are often unable to understand the rationale behind their decisions. The lack of transparency of such systems can have severe consequences or poor uses of limited valuable resources in medical diagnosis, financial decision-making, and in other high-stake domains. Therefore, the issue of ML explanation has experienced a surge in interest from the research community to application domains. W
Record details
Published: 4 March 2021
Source: OpenAlex
Category: Research
Topics: Healthcare · Transparency
Retrieved: 14 July 2026
Related evidence
These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.
Who is afraid of black box algorithms? On the epistemological and ethical basis of trust in medical AI
OpenAlex · 18 March 2021
A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease
OpenAlex · 29 January 2021
A Survey on the Explainability of Supervised Machine Learning
OpenAlex · 19 January 2021
The role of explainability in creating trustworthy artificial intelligence for health care: A comprehensive survey of the terminology, design choices, and evaluation strategies
OpenAlex · 10 December 2020
Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review
OpenAlex · 31 May 2021
Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol
OpenAlex · 1 June 2021
How to cite this record
ethics.ai (4 March 2021), “Evaluating the Quality of Machine Learning Explanations: A Survey on Methods and Metrics,” evidence record 8908, https://ethics.ai/record/8908 (originally published by OpenAlex).
Use and limitations
This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.