Abstract Machine learning (ML) models are nowadays used in complex applications in various domains, such as medicine, bioinformatics, and other sciences. Due to their black …
The remarkable progress in algorithms for machine and deep learning have opened the doors to new opportunities, and some dark possibilities. However, a bright future awaits …
Abstract Previous research in Explainable Artificial Intelligence (XAI) suggests that a main aim of explainability approaches is to satisfy specific interests, goals, expectations, needs …
B Shneiderman - ACM Transactions on Interactive Intelligent Systems …, 2020 - dl.acm.org
This article attempts to bridge the gap between widely discussed ethical principles of Human- centered AI (HCAI) and practical steps for effective governance. Since HCAI systems are …
QV Liao, D Gruen, S Miller - Proceedings of the 2020 CHI conference on …, 2020 - dl.acm.org
A surge of interest in explainable AI (XAI) has led to a vast collection of algorithmic work on the topic. While many recognize the necessity to incorporate explainability features in AI …
In recent years, the field of explainable AI (XAI) has produced a vast collection of algorithms, providing a useful toolbox for researchers and practitioners to build XAI applications. With …
Machine learning (ML) models are now routinely deployed in domains ranging from criminal justice to healthcare. With this newfound ubiquity, ML has moved beyond academia and …
X Wang, M Yin - 26th international conference on intelligent user …, 2021 - dl.acm.org
This paper contributes to the growing literature in empirical evaluation of explainable AI (XAI) methods by presenting a comparison on the effects of a set of established XAI methods …
With the recent wave of progress in artificial intelligence (AI) has come a growing awareness of the large-scale impacts of AI systems, and recognition that existing regulations and norms …