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) …
Explainable artificial intelligence applications in cyber security: State-of-the-art in research
This survey presents a comprehensive review of current literature on Explainable Artificial
Intelligence (XAI) methods for cyber security applications. Due to the rapid development of …
Intelligence (XAI) methods for cyber security applications. Due to the rapid development of …
Prediction of stock price direction using a hybrid GA-XGBoost algorithm with a three-stage feature engineering process
The stock market has performed one of the most important functions in a laissez-faire
economic system by gathering people, companies, and flows of money for several centuries …
economic system by gathering people, companies, and flows of money for several centuries …
Toward transparent ai: A survey on interpreting the inner structures of deep neural networks
The last decade of machine learning has seen drastic increases in scale and capabilities.
Deep neural networks (DNNs) are increasingly being deployed in the real world. However …
Deep neural networks (DNNs) are increasingly being deployed in the real world. However …
Benchmarking and survey of explanation methods for black box models
The rise of sophisticated black-box machine learning models in Artificial Intelligence
systems has prompted the need for explanation methods that reveal how these models work …
systems has prompted the need for explanation methods that reveal how these models work …
[HTML][HTML] Effects of Explainable Artificial Intelligence on trust and human behavior in a high-risk decision task
Understanding the recommendations of an artificial intelligence (AI) based assistant for
decision-making is especially important in high-risk tasks, such as deciding whether a …
decision-making is especially important in high-risk tasks, such as deciding whether a …
Explainable AI for time series classification: a review, taxonomy and research directions
Time series data is increasingly used in a wide range of fields, and it is often relied on in
crucial applications and high-stakes decision-making. For instance, sensors generate time …
crucial applications and high-stakes decision-making. For instance, sensors generate time …
Connecting algorithmic research and usage contexts: a perspective of contextualized evaluation for explainable AI
Recent years have seen a surge of interest in the field of explainable AI (XAI), with a
plethora of algorithms proposed in the literature. However, a lack of consensus on how to …
plethora of algorithms proposed in the literature. However, a lack of consensus on how to …
Understanding and improving visual prompting: A label-mapping perspective
We revisit and advance visual prompting (VP), an input prompting technique for vision tasks.
VP can reprogram a fixed, pre-trained source model to accomplish downstream tasks in the …
VP can reprogram a fixed, pre-trained source model to accomplish downstream tasks in the …
HIVE: Evaluating the human interpretability of visual explanations
As AI technology is increasingly applied to high-impact, high-risk domains, there have been
a number of new methods aimed at making AI models more human interpretable. Despite …
a number of new methods aimed at making AI models more human interpretable. Despite …