How cognitive biases affect XAI-assisted decision-making: A systematic review
The field of eXplainable Artificial Intelligence (XAI) aims to bring transparency to complex AI
systems. Although it is usually considered an essentially technical field, effort has been …
systems. Although it is usually considered an essentially technical field, effort has been …
Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial
On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an
essential layer of safety assurance that could lead to more principled decision making by …
essential layer of safety assurance that could lead to more principled decision making by …
Generative AI and ChatGPT: Applications, challenges, and AI-human collaboration
F Fui-Hoon Nah, R Zheng, J Cai, K Siau… - Journal of Information …, 2023 - Taylor & Francis
Artificial intelligence (AI) has elicited much attention across disciplines and industries (Hyder
et al., 2019). AI has been defined as “a system's ability to correctly interpret external data, to …
et al., 2019). AI has been defined as “a system's ability to correctly interpret external data, to …
[PDF][PDF] Ai transparency in the age of llms: A human-centered research roadmap
QV Liao, JW Vaughan - arXiv preprint arXiv:2306.01941, 2023 - assets.pubpub.org
The rise of powerful large language models (LLMs) brings about tremendous opportunities
for innovation but also looming risks for individuals and society at large. We have reached a …
for innovation but also looming risks for individuals and society at large. We have reached a …
Data cards: Purposeful and transparent dataset documentation for responsible ai
As research and industry moves towards large-scale models capable of numerous
downstream tasks, the complexity of understanding multi-modal datasets that give nuance to …
downstream tasks, the complexity of understanding multi-modal datasets that give nuance to …
Towards trustworthy machine fault diagnosis: A probabilistic Bayesian deep learning framework
Fault diagnosis is efficient to improve the safety, reliability, and cost-effectiveness of
industrial machinery. Deep learning has been extensively investigated in fault diagnosis …
industrial machinery. Deep learning has been extensively investigated in fault diagnosis …
Investigating explainability of generative AI for code through scenario-based design
What does it mean for a generative AI model to be explainable? The emergent discipline of
explainable AI (XAI) has made great strides in helping people understand discriminative …
explainable AI (XAI) has made great strides in helping people understand discriminative …
Look before you leap: An exploratory study of uncertainty measurement for large language models
The recent performance leap of Large Language Models (LLMs) opens up new
opportunities across numerous industrial applications and domains. However, erroneous …
opportunities across numerous industrial applications and domains. However, erroneous …
Understanding the role of human intuition on reliance in human-AI decision-making with explanations
AI explanations are often mentioned as a way to improve human-AI decision-making, but
empirical studies have not found consistent evidence of explanations' effectiveness and, on …
empirical studies have not found consistent evidence of explanations' effectiveness and, on …
Accountability in an algorithmic society: relationality, responsibility, and robustness in machine learning
In 1996, Accountability in a Computerized Society [95] issued a clarion call concerning the
erosion of accountability in society due to the ubiquitous delegation of consequential …
erosion of accountability in society due to the ubiquitous delegation of consequential …