An Explanatory Model Steering System for Collaboration between Domain Experts and AI

• URL: http://arxiv.org/abs/2405.13038v1
• Date: Fri, 17 May 2024 07:27:48 GMT
• Title: An Explanatory Model Steering System for Collaboration between Domain Experts and AI
• Authors: Aditya Bhattacharya, Simone Stumpf, Katrien Verbert,
• Abstract summary: We introduce an Explanatory Model Steering system that allows domain experts to steer prediction models using their domain knowledge. The system includes an explanation dashboard that combines different types of data-centric and model-centric explanations. Our findings highlight the importance of involving domain experts during model steering, ultimately leading to improved human-AI collaboration.
• Score: 4.398998634035531
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the increasing adoption of Artificial Intelligence (AI) systems in high-stake domains, such as healthcare, effective collaboration between domain experts and AI is imperative. To facilitate effective collaboration between domain experts and AI systems, we introduce an Explanatory Model Steering system that allows domain experts to steer prediction models using their domain knowledge. The system includes an explanation dashboard that combines different types of data-centric and model-centric explanations and allows prediction models to be steered through manual and automated data configuration approaches. It allows domain experts to apply their prior knowledge for configuring the underlying training data and refining prediction models. Additionally, our model steering system has been evaluated for a healthcare-focused scenario with 174 healthcare experts through three extensive user studies. Our findings highlight the importance of involving domain experts during model steering, ultimately leading to improved human-AI collaboration.

Related papers

• AI Readiness in Healthcare through Storytelling XAI [0.5120567378386615]
  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.
  arXiv  Detail & Related papers  (2024-10-24T13:30:18Z)
• Generative Diffusion-based Contract Design for Efficient AI Twins Migration in Vehicular Embodied AI Networks [55.15079732226397]
  Embodied AI is a rapidly advancing field that bridges the gap between cyberspace and physical space. In VEANET, embodied AI twins act as in-vehicle AI assistants to perform diverse tasks supporting autonomous driving.
  arXiv  Detail & Related papers  (2024-10-02T02:20:42Z)
• Data Analysis in the Era of Generative AI [56.44807642944589]
  This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges. We explore how the emergence of large language and multimodal models offers new opportunities to enhance various stages of data analysis workflow. We then examine human-centered design principles that facilitate intuitive interactions, build user trust, and streamline the AI-assisted analysis workflow across multiple apps.
  arXiv  Detail & Related papers  (2024-09-27T06:31:03Z)
• Enhancing Breast Cancer Diagnosis in Mammography: Evaluation and Integration of Convolutional Neural Networks and Explainable AI [0.0]
  The study presents an integrated framework combining Convolutional Neural Networks (CNNs) and Explainable Artificial Intelligence (XAI) for the enhanced diagnosis of breast cancer. The methodology encompasses an elaborate data preprocessing pipeline and advanced data augmentation techniques to counteract dataset limitations. A focal point of our study is the evaluation of XAI's effectiveness in interpreting model predictions.
  arXiv  Detail & Related papers  (2024-04-05T05:00:21Z)
• Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness [53.91018508439669]
  The study explores the complexities of integrating Artificial Intelligence into Autonomous Vehicles (AVs) It examines the challenges introduced by AI components and the impact on testing procedures. The paper identifies significant challenges and suggests future directions for research and development of AI in AV technology.
  arXiv  Detail & Related papers  (2024-02-21T08:29:42Z)
• Lessons Learned from EXMOS User Studies: A Technical Report Summarizing Key Takeaways from User Studies Conducted to Evaluate The EXMOS Platform [5.132827811038276]
  Two user studies aimed at illuminating the influence of different explanation types on three key dimensions: trust, understandability, and model improvement. Results show that global model-centric explanations alone are insufficient for effectively guiding users during the intricate process of data configuration. We present essential implications for developing interactive machine-learning systems driven by explanations.
  arXiv  Detail & Related papers  (2023-10-03T14:04:45Z)
• DASS Good: Explainable Data Mining of Spatial Cohort Data [3.1442270083085964]
  We describe the co-design of a modeling system, DASS, to support the hybrid human-machine development and validation of predictive models. DASS incorporates human-in-the-loop visual steering, spatial data, and explainable AI to augment domain knowledge with automatic data mining.
  arXiv  Detail & Related papers  (2023-04-10T21:24:21Z)
• A general-purpose AI assistant embedded in an open-source radiology information system [0.0]
  We describe the novel Human-AI partnership capabilities of the platform, including few-shot learning and swarm learning approaches. We developed an active learning strategy within the RIS, so that the human radiologist can enable/disable AI annotations as well as "fix"/relabel the AI annotations. This helps establish a partnership between the radiologist user and a user-specific AI model.
  arXiv  Detail & Related papers  (2023-03-18T05:27:43Z)
• 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)
• Model-Based Deep Learning [155.063817656602]
  Signal processing, communications, and control have traditionally relied on classical statistical modeling techniques. Deep neural networks (DNNs) use generic architectures which learn to operate from data, and demonstrate excellent performance. We are interested in hybrid techniques that combine principled mathematical models with data-driven systems to benefit from the advantages of both approaches.
  arXiv  Detail & Related papers  (2020-12-15T16:29:49Z)
• A general framework for scientifically inspired explanations in AI [76.48625630211943]
  We instantiate the concept of structure of scientific explanation as the theoretical underpinning for a general framework in which explanations for AI systems can be implemented. This framework aims to provide the tools to build a "mental-model" of any AI system so that the interaction with the user can provide information on demand and be closer to the nature of human-made explanations.
  arXiv  Detail & Related papers  (2020-03-02T10:32:21Z)

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.