A review of the explainability and safety of conversational agents for mental health to identify avenues for improvement

S Sarkar, M Gaur, LK Chen, M Garg… - Frontiers in Artificial …, 2023 - frontiersin.org
Virtual Mental Health Assistants (VMHAs) continuously evolve to support the overloaded
global healthcare system, which receives approximately 60 million primary care visits and 6 …

Neurosymbolic artificial intelligence (why, what, and how)

A Sheth, K Roy, M Gaur - IEEE Intelligent Systems, 2023 - ieeexplore.ieee.org
Humans interact with the environment using a combination of perception—transforming
sensory inputs from their environment into symbols, and cognition—mapping symbols to …

Tutorial: Neuro-symbolic ai for mental healthcare

K Roy, U Lokala, M Gaur, AP Sheth - Proceedings of the Second …, 2022 - dl.acm.org
Artificial Intelligence (AI) systems for mental healthcare (MHCare) have been ever-growing
after realizing the importance of early interventions for patients with chronic mental health …

Process knowledge-infused ai: Toward user-level explainability, interpretability, and safety

A Sheth, M Gaur, K Roy, R Venkataraman… - IEEE Internet …, 2022 - ieeexplore.ieee.org
AI has seen wide adoption for automating tasks in several domains. However, AI's use in
high-value, sensitive, or safety-critical applications such as self-management for …

Neurosymbolic Value-Inspired AI (Why, What, and How)

A Sheth, K Roy - arXiv preprint arXiv:2312.09928, 2023 - arxiv.org
The rapid progression of Artificial Intelligence (AI) systems, facilitated by the advent of Large
Language Models (LLMs), has resulted in their widespread application to provide human …

Knowledge-infused learning: A sweet spot in neuro-symbolic ai

M Gaur, K Gunaratna, S Bhatt… - IEEE Internet …, 2022 - ieeexplore.ieee.org
Deep learning has revolutionized the artificial intelligence (AI) landscape by enhancing
machine capabilities to understand data-dependant relationships. On the other hand …

A cross attention approach to diagnostic explainability using clinical practice guidelines for depression

S Dalal, D Tilwani, M Gaur, S Jain… - IEEE Journal of …, 2024 - ieeexplore.ieee.org
The lack of explainability in using relevant clinical knowledge hinders the adoption of
artificial intelligence-powered analysis of unstructured clinical dialogue. A wealth of relevant …

Process knowledge-infused learning for clinician-friendly explanations

K Roy, Y Zi, M Gaur, J Malekar, Q Zhang… - Proceedings of the …, 2023 - ojs.aaai.org
Abstract Language models have the potential to assess mental health using social media
data. By analyzing online posts and conversations, these models can detect patterns …

Building trustworthy NeuroSymbolic AI Systems: Consistency, reliability, explainability, and safety

M Gaur, A Sheth - AI Magazine, 2024 - Wiley Online Library
Explainability and Safety engender trust. These require a model to exhibit consistency and
reliability. To achieve these, it is necessary to use and analyze data and knowledge with …

Knowledge-infused self attention transformers

K Roy, Y Zi, V Narayanan, M Gaur, A Sheth - arXiv preprint arXiv …, 2023 - arxiv.org
Transformer-based language models have achieved impressive success in various natural
language processing tasks due to their ability to capture complex dependencies and …