Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Kenny, Eoin M"'
Autor:
Kenny, Eoin M., Dharmavaram, Akshay, Lee, Sang Uk, Phan-Minh, Tung, Rajesh, Shreyas, Hu, Yunqing, Major, Laura, Tomov, Momchil S., Shah, Julie A.
Self-driving cars increasingly rely on deep neural networks to achieve human-like driving. However, the opacity of such black-box motion planners makes it challenging for the human behind the wheel to accurately anticipate when they will fail, with p
Externí odkaz:
http://arxiv.org/abs/2411.18714
Autor:
Kenny, Eoin M., Huang, Weipeng
When users receive either a positive or negative outcome from an automated system, Explainable AI (XAI) has almost exclusively focused on how to mutate negative outcomes into positive ones by crossing a decision boundary using counterfactuals (e.g.,
Externí odkaz:
http://arxiv.org/abs/2310.18937
Publikováno v:
In Computers and Electronics in Agriculture April 2024 219
Publikováno v:
IJCAI-21 Workshop on DL-CBR-AML, July 2021
Recently, it has been proposed that fruitful synergies may exist between Deep Learning (DL) and Case Based Reasoning (CBR); that there are insights to be gained by applying CBR ideas to problems in DL (what could be called DeepCBR). In this paper, we
Externí odkaz:
http://arxiv.org/abs/2104.14461
Publikováno v:
Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21), August, 2021
In recent years, there has been an explosion of AI research on counterfactual explanations as a solution to the problem of eXplainable AI (XAI). These explanations seem to offer technical, psychological and legal benefits over other explanation techn
Externí odkaz:
http://arxiv.org/abs/2103.01035
Corporate mergers and acquisitions (M&A) account for billions of dollars of investment globally every year, and offer an interesting and challenging domain for artificial intelligence. However, in these highly sensitive domains, it is crucial to not
Externí odkaz:
http://arxiv.org/abs/2010.12512
Autor:
Kenny, Eoin M., Keane, Mark T.
There is a growing concern that the recent progress made in AI, especially regarding the predictive competence of deep learning models, will be undermined by a failure to properly explain their operation and outputs. In response to this disquiet coun
Externí odkaz:
http://arxiv.org/abs/2009.06399
Publikováno v:
IJCAI-20 Workshop on Explainable AI (XAI), September 2020
This paper reports two experiments (N=349) on the impact of post hoc explanations by example and error rates on peoples perceptions of a black box classifier. Both experiments show that when people are given case based explanations, from an implement
Externí odkaz:
http://arxiv.org/abs/2009.06349
Autor:
Keane, Mark T., Kenny, Eoin M.
Publikováno v:
IJCAI 2019 Workshop on Explainable Artificial Intelligence (XAI)
The notion of twin systems is proposed to address the eXplainable AI (XAI) problem, where an uninterpretable black-box system is mapped to a white-box 'twin' that is more interpretable. In this short paper, we overview very recent work that advances
Externí odkaz:
http://arxiv.org/abs/1905.08069
Autor:
Keane, Mark T, Kenny, Eoin M
Publikováno v:
Proceedings of the 27th International Conference on Case Based Reasoning (ICCBR-19), 2019
This paper surveys an approach to the XAI problem, using post-hoc explanation by example, that hinges on twinning Artificial Neural Networks (ANNs) with Case-Based Reasoning (CBR) systems, so-called ANN-CBR twins. A systematic survey of 1100+ papers
Externí odkaz:
http://arxiv.org/abs/1905.07186