From anecdotal evidence to quantitative evaluation methods: A systematic review on evaluating explainable ai
The rising popularity of explainable artificial intelligence (XAI) to understand high-performing
black boxes raised the question of how to evaluate explanations of machine learning (ML) …
black boxes raised the question of how to evaluate explanations of machine learning (ML) …
A comprehensive taxonomy for explainable artificial intelligence: a systematic survey of surveys on methods and concepts
G Schwalbe, B Finzel - Data Mining and Knowledge Discovery, 2024 - Springer
In the meantime, a wide variety of terminologies, motivations, approaches, and evaluation
criteria have been developed within the research field of explainable artificial intelligence …
criteria have been developed within the research field of explainable artificial intelligence …
Interpretable machine learning–a brief history, state-of-the-art and challenges
We present a brief history of the field of interpretable machine learning (IML), give an
overview of state-of-the-art interpretation methods and discuss challenges. Research in IML …
overview of state-of-the-art interpretation methods and discuss challenges. Research in IML …
A global taxonomy of interpretable AI: unifying the terminology for the technical and social sciences
M Graziani, L Dutkiewicz, D Calvaresi… - Artificial intelligence …, 2023 - Springer
Since its emergence in the 1960s, Artificial Intelligence (AI) has grown to conquer many
technology products and their fields of application. Machine learning, as a major part of the …
technology products and their fields of application. Machine learning, as a major part of the …
Explainable artificial intelligence in information systems: A review of the status quo and future research directions
The quest to open black box artificial intelligence (AI) systems evolved into an emerging
phenomenon of global interest for academia, business, and society and brought about the …
phenomenon of global interest for academia, business, and society and brought about the …
Towards human-centered explainable ai: A survey of user studies for model explanations
Explainable AI (XAI) is widely viewed as a sine qua non for ever-expanding AI research. A
better understanding of the needs of XAI users, as well as human-centered evaluations of …
better understanding of the needs of XAI users, as well as human-centered evaluations of …
Explainable convolutional neural networks: A taxonomy, review, and future directions
Convolutional neural networks (CNNs) have shown promising results and have
outperformed classical machine learning techniques in tasks such as image classification …
outperformed classical machine learning techniques in tasks such as image classification …
[HTML][HTML] Designing explainable AI to improve human-AI team performance: a medical stakeholder-driven scoping review
The rise of complex AI systems in healthcare and other sectors has led to a growing area of
research called Explainable AI (XAI) designed to increase transparency. In this area …
research called Explainable AI (XAI) designed to increase transparency. In this area …
Designing an XAI interface for BCI experts: A contextual design for pragmatic explanation interface based on domain knowledge in a specific context
Abstract Domain experts utilize a decision-support system depending on an artificial
intelligence (AI) algorithm. Likewise, researchers in brain-computer interface (BCI) have …
intelligence (AI) algorithm. Likewise, researchers in brain-computer interface (BCI) have …
[HTML][HTML] Generating multi-level explanations for process outcome predictions
Process mining focuses on the analysis of event log data to build various process analytical
capabilities. Predictive process analytics has emerged as one of such key capabilities and it …
capabilities. Predictive process analytics has emerged as one of such key capabilities and it …