Publications: Explainable AI
AI systems’ ability to explain their reasoning is critical to their utility since human users do not trust decisions from opaque "black boxes." Explainable AI studies the development of systems that provide visual, textual, or multi-modal explanations that help elucidate the reasoning behind their decisions.
- Towards Automated Error Analysis: Learning to Characterize Errors
[Details] [PDF] [Poster]
Tong Gao, Shivang Singh, Raymond J. Mooney
Short version appears in the 19th International Florida Artificial Intelligence Research Society Conference (FLAIRS), May 2022.
- Incorporating Textual Resources to Improve Visual Question Answering
[Details] [PDF] [Slides (PDF)]
Jialin Wu
September 2021. Ph.D. Proposal.
- Improving VQA and its Explanations by Comparing Competing Explanations
[Details] [PDF] [Slides (PDF)]
Jialin Wu, Liyan Chen, Raymond J. Mooney
In The AAAI Conference on Artificial Intelligence (AAAI), Explainable Agency in Artificial Intelligence Workshop, February 2021.
- Self-Critical Reasoning for Robust Visual Question Answering
[Details] [PDF] [Slides (PDF)] [Poster]
Jialin Wu and Raymond J. Mooney
In Proceedings of Neural Information Processing Systems (NeurIPS) , December 2019.
- Faithful Multimodal Explanation for Visual Question Answering
[Details] [PDF] [Slides (PPT)]
Jialin Wu and Raymond J. Mooney
In Proceedings of the Second BlackboxNLP Workshop at ACL, 103-112, Florence, Italy, August 2019.
- Do Human Rationales Improve Machine Explanations?
[Details] [PDF] [Poster]
Julia Strout, Ye Zhang, Raymond J. Mooney
In Proceedings of the Second BlackboxNLP Workshop at ACL, 56-62, Florence, Italy, August 2019.
- Explainable Improved Ensembling for Natural Language and Vision
[Details] [PDF] [Slides (PPT)] [Slides (PDF)]
Nazneen Rajani
PhD Thesis, Department of Computer Science, The University of Texas at Austin, July 2018.
- Ensembling Visual Explanations for VQA
[Details] [PDF] [Poster]
Nazneen Fatema Rajani, Raymond J. Mooney
In Proceedings of the NIPS 2017 workshop on Visually-Grounded Interaction and Language (ViGIL), December 2017.
- Using Explanations to Improve Ensembling of Visual Question Answering Systems
[Details] [PDF] [Poster]
Nazneen Fatema Rajani and Raymond J. Mooney
In Proceedings of the IJCAI 2017 Workshop on Explainable Artificial Intelligence (XAI), 43-47, Melbourne, Australia, August 2017.
- Explaining Recommendations: Satisfaction vs. Promotion
[Details] [PDF]
Mustafa Bilgic and Raymond J. Mooney
In Proceedings of Beyond Personalization 2005: A Workshop on the Next Stage of Recommender Systems Research at the 2005 International Conference on Intelligent User Interfaces, San Diego, CA, January 2005.
- Explanation for Recommender Systems: Satisfaction vs. Promotion
[Details] [PDF]
Mustafa Bilgic
Austin, TX, May 2004. Undergraduate Honor Thesis, Department of Computer Sciences, University of Texas at Austin.
