Machine learning interpretability: A survey on methods and metrics
Machine learning systems are becoming increasingly ubiquitous. These systems's adoption
has been expanding, accelerating the shift towards a more algorithmic society, meaning that …
has been expanding, accelerating the shift towards a more algorithmic society, meaning that …
Trusting automation: Designing for responsivity and resilience
Objective This paper reviews recent articles related to human trust in automation to guide
research and design for increasingly capable automation in complex work environments …
research and design for increasingly capable automation in complex work environments …
[HTML][HTML] Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence
Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated
applications, but the outcomes of many AI models are challenging to comprehend and trust …
applications, but the outcomes of many AI models are challenging to comprehend and trust …
Should chatgpt be biased? challenges and risks of bias in large language models
E Ferrara - arxiv preprint arxiv:2304.03738, 2023 - arxiv.org
As the capabilities of generative language models continue to advance, the implications of
biases ingrained within these models have garnered increasing attention from researchers …
biases ingrained within these models have garnered increasing attention from researchers …
What do we want from Explainable Artificial Intelligence (XAI)?–A stakeholder perspective on XAI and a conceptual model guiding interdisciplinary XAI research
Abstract Previous research in Explainable Artificial Intelligence (XAI) suggests that a main
aim of explainability approaches is to satisfy specific interests, goals, expectations, needs …
aim of explainability approaches is to satisfy specific interests, goals, expectations, needs …
The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI
D Shin - International journal of human-computer studies, 2021 - Elsevier
Artificial intelligence and algorithmic decision-making processes are increasingly criticized
for their black-box nature. Explainable AI approaches to trace human-interpretable decision …
for their black-box nature. Explainable AI approaches to trace human-interpretable decision …
[HTML][HTML] Explainable Artificial Intelligence (XAI) from a user perspective: A synthesis of prior literature and problematizing avenues for future research
The rapid growth and use of artificial intelligence (AI)-based systems have raised concerns
regarding explainability. Recent studies have discussed the emerging demand for …
regarding explainability. Recent studies have discussed the emerging demand for …
How to explain AI systems to end users: a systematic literature review and research agenda
Purpose Inscrutable machine learning (ML) models are part of increasingly many
information systems. Understanding how these models behave, and what their output is …
information systems. Understanding how these models behave, and what their output is …
User perceptions of algorithmic decisions in the personalized AI system: Perceptual evaluation of fairness, accountability, transparency, and explainability
D Shin - Journal of Broadcasting & Electronic Media, 2020 - Taylor & Francis
With the growing presence of algorithms and their far-reaching effects, artificial intelligence
(AI) will be mainstream trends any time soon. Despite this surging popularity, little is known …
(AI) will be mainstream trends any time soon. Despite this surging popularity, little is known …
Explainable recommendation: A survey and new perspectives
Explainable recommendation attempts to develop models that generate not only high-quality
recommendations but also intuitive explanations. The explanations may either be post-hoc …
recommendations but also intuitive explanations. The explanations may either be post-hoc …