Department of Computer Science

Machine Learning Research Group

University of Texas at Austin Artificial Intelligence Lab

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.
  1. Self-Critical Reasoning for Robust Visual Question Answering
    [Details] [PDF]
    Jialin Wu and Raymond J. Mooney
    To Appear In Proceedings of Neural Information Processing Systems (NeurIPS) , December 2019.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.