AI Readiness in Healthcare through Storytelling XAI

• URL: http://arxiv.org/abs/2410.18725v1
• Date: Thu, 24 Oct 2024 13:30:18 GMT
• Title: AI Readiness in Healthcare through Storytelling XAI
• Authors: Akshat Dubey, Zewen Yang, Georges Hattab,
• Abstract summary: We develop an approach that combines multi-task distillation with interpretability techniques to enable audience-centric explainability. Our methods increase the trust of both the domain experts and the machine learning experts to enable a responsible AI.
• Score: 0.5120567378386615
• License:
• Abstract: Artificial Intelligence is rapidly advancing and radically impacting everyday life, driven by the increasing availability of computing power. Despite this trend, the adoption of AI in real-world healthcare is still limited. One of the main reasons is the trustworthiness of AI models and the potential hesitation of domain experts with model predictions. Explainable Artificial Intelligence (XAI) techniques aim to address these issues. However, explainability can mean different things to people with different backgrounds, expertise, and goals. To address the target audience with diverse needs, we develop storytelling XAI. In this research, we have developed an approach that combines multi-task distillation with interpretability techniques to enable audience-centric explainability. Using multi-task distillation allows the model to exploit the relationships between tasks, potentially improving interpretability as each task supports the other leading to an enhanced interpretability from the perspective of a domain expert. The distillation process allows us to extend this research to large deep models that are highly complex. We focus on both model-agnostic and model-specific methods of interpretability, supported by textual justification of the results in healthcare through our use case. Our methods increase the trust of both the domain experts and the machine learning experts to enable a responsible AI.

Related papers

• Study on the Helpfulness of Explainable Artificial Intelligence [0.0]
  Legal, business, and ethical requirements motivate using effective XAI. We propose to evaluate XAI methods via the user's ability to successfully perform a proxy task. In other words, we address the helpfulness of XAI for human decision-making.
  arXiv  Detail & Related papers  (2024-10-14T14:03:52Z)
• Applications of Explainable artificial intelligence in Earth system science [12.454478986296152]
  This review aims to provide a foundational understanding of explainable AI (XAI) XAI offers a set of powerful tools that make the models more transparent. We identify four significant challenges that XAI faces within the Earth system science (ESS) A visionary outlook for ESS envisions a harmonious blend where process-based models govern the known, AI models explore the unknown, and XAI bridges the gap by providing explanations.
  arXiv  Detail & Related papers  (2024-06-12T15:05:29Z)
• Position Paper: Agent AI Towards a Holistic Intelligence [53.35971598180146]
  We emphasize developing Agent AI -- an embodied system that integrates large foundation models into agent actions. In this paper, we propose a novel large action model to achieve embodied intelligent behavior, the Agent Foundation Model.
  arXiv  Detail & Related papers  (2024-02-28T16:09:56Z)
• A Brief Review of Explainable Artificial Intelligence in Healthcare [7.844015105790313]
  XAI refers to the techniques and methods for building AI applications. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices.
  arXiv  Detail & Related papers  (2023-04-04T05:41:57Z)
• DIME: Fine-grained Interpretations of Multimodal Models via Disentangled Local Explanations [119.1953397679783]
  We focus on advancing the state-of-the-art in interpreting multimodal models. Our proposed approach, DIME, enables accurate and fine-grained analysis of multimodal models.
  arXiv  Detail & Related papers  (2022-03-03T20:52:47Z)
• Cybertrust: From Explainable to Actionable and Interpretable AI (AI2) [58.981120701284816]
  Actionable and Interpretable AI (AI2) will incorporate explicit quantifications and visualizations of user confidence in AI recommendations. It will allow examining and testing of AI system predictions to establish a basis for trust in the systems' decision making.
  arXiv  Detail & Related papers  (2022-01-26T18:53:09Z)
• WenLan 2.0: Make AI Imagine via a Multimodal Foundation Model [74.4875156387271]
  We develop a novel foundation model pre-trained with huge multimodal (visual and textual) data. We show that state-of-the-art results can be obtained on a wide range of downstream tasks.
  arXiv  Detail & Related papers  (2021-10-27T12:25:21Z)
• Explainable Artificial Intelligence Approaches: A Survey [0.22940141855172028]
  Lack of explainability of a decision from an Artificial Intelligence based "black box" system/model is a key stumbling block for adopting AI in high stakes applications. We demonstrate popular Explainable Artificial Intelligence (XAI) methods with a mutual case study/task. We analyze for competitive advantages from multiple perspectives. We recommend paths towards responsible or human-centered AI using XAI as a medium.
  arXiv  Detail & Related papers  (2021-01-23T06:15:34Z)
• Explainable AI meets Healthcare: A Study on Heart Disease Dataset [0.0]
  The aim is to enlighten practitioners on the understandability and interpretability of explainable AI systems using a variety of techniques. Our paper contains examples based on the heart disease dataset and elucidates on how the explainability techniques should be preferred to create trustworthiness.
  arXiv  Detail & Related papers  (2020-11-06T05:18:43Z)
• Explainability in Deep Reinforcement Learning [68.8204255655161]
  We review recent works in the direction to attain Explainable Reinforcement Learning (XRL) In critical situations where it is essential to justify and explain the agent's behaviour, better explainability and interpretability of RL models could help gain scientific insight on the inner workings of what is still considered a black box.
  arXiv  Detail & Related papers  (2020-08-15T10:11:42Z)
• Machine Common Sense [77.34726150561087]
  Machine common sense remains a broad, potentially unbounded problem in artificial intelligence (AI) This article deals with the aspects of modeling commonsense reasoning focusing on such domain as interpersonal interactions.
  arXiv  Detail & Related papers  (2020-06-15T13:59:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.