Gaur, ManasGaur, Manas2023-01-172023-01-172024-07-15Gaur, Manas. “Targeted Knowledge Infusion To Make Conversational AI Explainable and Safe.” Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 13 (2023): 15438–15438. https://doi.org/10.1609/aaai.v37i13.26805.https://doi.org/10.1609/aaai.v37i13.26805AAAI-NFT-Materials: https://drive.google.com/drive/folders/1dq7wGEd6c_xWpIht4IAArOEatkCRs9XW?usp=sharingThirty-Seventh AAAI Conference on Artificial Intelligence, February 7 – 14, 2023, Walter E. Washington Convention Center Washington DC, USAConversational Systems (CSys) represent practical and tangible outcomes of advances in NLP and AI. CSys see continuous improvements through unsupervised training of large language models (LLMs) on a humongous amount of generic training data. However, when these CSys are suggested for use in domains like Mental Health, they fail to match the acceptable standards of clinical care, such as the clinical process in Patient Health Questionnaire (PHQ-9). The talk will present, Knowledge-infused Learning (KiL), a paradigm within NeuroSymbolic AI that focuses on making machine/deep learning models (i) learn over knowledge-enriched data, (ii) learn to follow guidelines in process-oriented tasks for safe and reasonable generation, and (iii) learn to leverage multiple contexts and stratified knowledge to yield user-level explanations. KiL established Knowledge-Intensive Language Understanding, a set of tasks for assessing safety, explainability, and conceptual flow in CSys.1 pageen-USThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.Knowledge-infused LearningProcess KnowledgeNeuroSymbolic AIConversational SystemsExplainabilitySafetyUMBC Ebiquity Research GroupText