Explainable Interface for Human-Autonomy Teaming: A Survey
- URL: http://arxiv.org/abs/2405.02583v1
- Date: Sat, 4 May 2024 06:35:38 GMT
- Title: Explainable Interface for Human-Autonomy Teaming: A Survey
- Authors: Xiangqi Kong, Yang Xing, Antonios Tsourdos, Ziyue Wang, Weisi Guo, Adolfo Perrusquia, Andreas Wikander,
- Abstract summary: This paper conducts a thoughtful study on the underexplored domain of Explainable Interface (EI) in HAT systems.
We explore the design, development, and evaluation of EI within XAI-enhanced HAT systems.
We contribute to a novel framework for EI, addressing the unique challenges in HAT.
- Score: 12.26178592621411
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Nowadays, large-scale foundation models are being increasingly integrated into numerous safety-critical applications, including human-autonomy teaming (HAT) within transportation, medical, and defence domains. Consequently, the inherent 'black-box' nature of these sophisticated deep neural networks heightens the significance of fostering mutual understanding and trust between humans and autonomous systems. To tackle the transparency challenges in HAT, this paper conducts a thoughtful study on the underexplored domain of Explainable Interface (EI) in HAT systems from a human-centric perspective, thereby enriching the existing body of research in Explainable Artificial Intelligence (XAI). We explore the design, development, and evaluation of EI within XAI-enhanced HAT systems. To do so, we first clarify the distinctions between these concepts: EI, explanations and model explainability, aiming to provide researchers and practitioners with a structured understanding. Second, we contribute to a novel framework for EI, addressing the unique challenges in HAT. Last, our summarized evaluation framework for ongoing EI offers a holistic perspective, encompassing model performance, human-centered factors, and group task objectives. Based on extensive surveys across XAI, HAT, psychology, and Human-Computer Interaction (HCI), this review offers multiple novel insights into incorporating XAI into HAT systems and outlines future directions.
Related papers
- 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) - Evaluating Human-AI Collaboration: A Review and Methodological Framework [4.41358655687435]
The use of artificial intelligence (AI) in working environments with individuals, known as Human-AI Collaboration (HAIC), has become essential.
evaluating HAIC's effectiveness remains challenging due to the complex interaction of components involved.
This paper provides a detailed analysis of existing HAIC evaluation approaches and develops a fresh paradigm for more effectively evaluating these systems.
arXiv Detail & Related papers (2024-07-09T12:52:22Z) - How Human-Centered Explainable AI Interface Are Designed and Evaluated: A Systematic Survey [48.97104365617498]
The emerging area of em Explainable Interfaces (EIs) focuses on the user interface and user experience design aspects of XAI.
This paper presents a systematic survey of 53 publications to identify current trends in human-XAI interaction and promising directions for EI design and development.
arXiv Detail & Related papers (2024-03-21T15:44:56Z) - Advancing Explainable Autonomous Vehicle Systems: A Comprehensive Review and Research Roadmap [4.2330023661329355]
This study presents a review to discuss the complexities associated with explanation generation and presentation.
Our roadmap is underpinned by principles of responsible research and innovation.
By exploring these research directions, the study aims to guide the development and deployment of explainable AVs.
arXiv Detail & Related papers (2024-03-19T11:43:41Z) - 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) - Neurosymbolic Value-Inspired AI (Why, What, and How) [8.946847190099206]
We propose a neurosymbolic computational framework called Value-Inspired AI (VAI)
VAI aims to represent and integrate various dimensions of human values.
We offer insights into the current progress made in this direction and outline potential future directions for the field.
arXiv Detail & Related papers (2023-12-15T16:33:57Z) - Towards Data-and Knowledge-Driven Artificial Intelligence: A Survey on Neuro-Symbolic Computing [73.0977635031713]
Neural-symbolic computing (NeSy) has been an active research area of Artificial Intelligence (AI) for many years.
NeSy shows promise of reconciling the advantages of reasoning and interpretability of symbolic representation and robust learning in neural networks.
arXiv Detail & Related papers (2022-10-28T04:38:10Z) - A.I. Robustness: a Human-Centered Perspective on Technological
Challenges and Opportunities [8.17368686298331]
Robustness of Artificial Intelligence (AI) systems remains elusive and constitutes a key issue that impedes large-scale adoption.
We introduce three concepts to organize and describe the literature both from a fundamental and applied point of view.
We highlight the central role of humans in evaluating and enhancing AI robustness, considering the necessary knowledge humans can provide.
arXiv Detail & Related papers (2022-10-17T10:00:51Z) - Counterfactual Explanations as Interventions in Latent Space [62.997667081978825]
Counterfactual explanations aim to provide to end users a set of features that need to be changed in order to achieve a desired outcome.
Current approaches rarely take into account the feasibility of actions needed to achieve the proposed explanations.
We present Counterfactual Explanations as Interventions in Latent Space (CEILS), a methodology to generate counterfactual explanations.
arXiv Detail & Related papers (2021-06-14T20:48:48Z) - 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.