[HTML][HTML] Evaluating the quality of machine learning explanations: A survey on methods and metrics
The most successful Machine Learning (ML) systems remain complex black boxes to end-
users, and even experts are often unable to understand the rationale behind their decisions …
users, and even experts are often unable to understand the rationale behind their decisions …
Explainability of artificial intelligence methods, applications and challenges: A comprehensive survey
W Ding, M Abdel-Basset, H Hawash, AM Ali - Information Sciences, 2022 - Elsevier
The continuous advancement of Artificial Intelligence (AI) has been revolutionizing the
strategy of decision-making in different life domains. Regardless of this achievement, AI …
strategy of decision-making in different life domains. Regardless of this achievement, AI …
Capabilities of gpt-4 on medical challenge problems
H Nori, N King, SM McKinney, D Carignan… - arXiv preprint arXiv …, 2023 - arxiv.org
Large language models (LLMs) have demonstrated remarkable capabilities in natural
language understanding and generation across various domains, including medicine. We …
language understanding and generation across various domains, including medicine. We …
[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 …
Explanations can reduce overreliance on ai systems during decision-making
H Vasconcelos, M Jörke… - Proceedings of the …, 2023 - dl.acm.org
Prior work has identified a resilient phenomenon that threatens the performance of human-
AI decision-making teams: overreliance, when people agree with an AI, even when it is …
AI decision-making teams: overreliance, when people agree with an AI, even when it is …
“Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI
N Sambasivan, S Kapania, H Highfill… - proceedings of the …, 2021 - dl.acm.org
AI models are increasingly applied in high-stakes domains like health and conservation.
Data quality carries an elevated significance in high-stakes AI due to its heightened …
Data quality carries an elevated significance in high-stakes AI due to its heightened …
To trust or to think: cognitive forcing functions can reduce overreliance on AI in AI-assisted decision-making
People supported by AI-powered decision support tools frequently overrely on the AI: they
accept an AI's suggestion even when that suggestion is wrong. Adding explanations to the …
accept an AI's suggestion even when that suggestion is wrong. Adding explanations to the …
Does the whole exceed its parts? the effect of ai explanations on complementary team performance
Many researchers motivate explainable AI with studies showing that human-AI team
performance on decision-making tasks improves when the AI explains its recommendations …
performance on decision-making tasks improves when the AI explains its recommendations …
Towards a science of human-ai decision making: a survey of empirical studies
As AI systems demonstrate increasingly strong predictive performance, their adoption has
grown in numerous domains. However, in high-stakes domains such as criminal justice and …
grown in numerous domains. However, in high-stakes domains such as criminal justice and …
" Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction
Despite the proliferation of explainable AI (XAI) methods, little is understood about end-
users' explainability needs and behaviors around XAI explanations. To address this gap and …
users' explainability needs and behaviors around XAI explanations. To address this gap and …