From 64104ce76be7e38088f22d4ea5c681463c369ca3 Mon Sep 17 00:00:00 2001 From: "Anh M. Nguyen" Date: Tue, 21 Mar 2023 08:19:55 -0500 Subject: [PATCH] Added Hemmer et al. IUI 2023 --- README.md | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 9aa82a7..0d8e1c3 100644 --- a/README.md +++ b/README.md @@ -288,7 +288,12 @@ This is an on-going attempt to consolidate interesting efforts in the area of un * “Help Me Help the AI”: Understanding How Explainability Can Support Human-AI Interaction. _Kim et al. 2022_ [pdf](https://arxiv.org/abs/2210.03735 "Practical recommendations and feedback for human-AI explanation designs from interviews with 20 end-users of Merlin, a bird-identification app.) -# E. Others +# E. Human-AI collaboration + +### Computer vision +* Human-AI Collaboration: The Effect of AI Delegation on Human Task Performance and Task Satisfaction. _Hemmer et al. IUI 2023_ [pdf](https://arxiv.org/abs/2303.09224 "Letting AIs handle most images in image classification and leaving the harder ones to humans result in higher overall classification accuracy than humans alone".) + +# F. Others * Yang, S. C. H., & Shafto, P. Explainable Artificial Intelligence via Bayesian Teaching. NIPS 2017 [pdf](http://shaftolab.com/assets/papers/yangShafto_NIPS_2017_machine_teaching.pdf) * Explainable AI for Designers: A Human-Centered Perspective on Mixed-Initiative Co-Creation [pdf](http://www.antoniosliapis.com/papers/explainable_ai_for_designers.pdf) * ICADx: Interpretable computer aided diagnosis of breast masses. _Kim et al. 2018_ [pdf](https://arxiv.org/abs/1805.08960)