Explainable Generative AI (GenXAI): A Survey, Conceptualization, and Research Agenda

• URL: http://arxiv.org/abs/2404.09554v1
• Date: Mon, 15 Apr 2024 08:18:16 GMT
• Title: Explainable Generative AI (GenXAI): A Survey, Conceptualization, and Research Agenda
• Authors: Johannes Schneider,
• Abstract summary: We elaborate on why XAI has gained importance with the rise of GenAI and its challenges for explainability research. We also unveil novel and emerging desiderata that explanations should fulfill, covering aspects such as verifiability, interactivity, security, and cost.
• Score: 1.8592384822257952
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative AI (GenAI) marked a shift from AI being able to recognize to AI being able to generate solutions for a wide variety of tasks. As the generated solutions and applications become increasingly more complex and multi-faceted, novel needs, objectives, and possibilities have emerged for explainability (XAI). In this work, we elaborate on why XAI has gained importance with the rise of GenAI and its challenges for explainability research. We also unveil novel and emerging desiderata that explanations should fulfill, covering aspects such as verifiability, interactivity, security, and cost. To this end, we focus on surveying existing works. Furthermore, we provide a taxonomy of relevant dimensions that allows us to better characterize existing XAI mechanisms and methods for GenAI. We discuss different avenues to ensure XAI, from training data to prompting. Our paper offers a short but concise technical background of GenAI for non-technical readers, focusing on text and images to better understand novel or adapted XAI techniques for GenAI. However, due to the vast array of works on GenAI, we decided to forego detailed aspects of XAI related to evaluation and usage of explanations. As such, the manuscript interests both technically oriented people and other disciplines, such as social scientists and information systems researchers. Our research roadmap provides more than ten directions for future investigation.

Related papers

• Generative Artificial Intelligence Meets Synthetic Aperture Radar: A Survey [49.29751866761522]
  This paper aims to investigate the intersection of GenAI and SAR. First, we illustrate the common data generation-based applications in SAR field. Then, an overview of the latest GenAI models is systematically reviewed. Finally, the corresponding applications in SAR domain are also included.
  arXiv  Detail & Related papers  (2024-11-05T03:06:00Z)
• Generative artificial intelligence in dentistry: Current approaches and future challenges [0.0]
  generative AI (GenAI) models bridge the usability gap of AI by providing a natural language interface to interact with complex models. In dental education, the student now has the opportunity to solve a plethora of questions by only prompting a GenAI model. GenAI can also be used in dental research, where the applications range from new drug discovery to assistance in academic writing.
  arXiv  Detail & Related papers  (2024-07-24T03:33:47Z)
• Model-based Maintenance and Evolution with GenAI: A Look into the Future [47.93555901495955]
  We argue that Generative Artificial Intelligence (GenAI) can be used as a means to address the limitations of Model-Based Engineering (MBM&E) We propose that GenAI can be used in MBM&E for: reducing engineers' learning curve, maximizing efficiency with recommendations, or serving as a reasoning tool to understand domain problems.
  arXiv  Detail & Related papers  (2024-07-09T23:13:26Z)
• Generative AI for Visualization: State of the Art and Future Directions [7.273704442256712]
  This paper looks back on previous visualization studies leveraging GenAI. By summarizing different generation algorithms, their current applications and limitations, this paper endeavors to provide useful insights for future GenAI4VIS research.
  arXiv  Detail & Related papers  (2024-04-28T11:27:30Z)
• Identifying and Mitigating the Security Risks of Generative AI [179.2384121957896]
  This paper reports the findings of a workshop held at Google on the dual-use dilemma posed by GenAI. GenAI can be used just as well by attackers to generate new attacks and increase the velocity and efficacy of existing attacks. We discuss short-term and long-term goals for the community on this topic.
  arXiv  Detail & Related papers  (2023-08-28T18:51:09Z)
• Towards Human Cognition Level-based Experiment Design for Counterfactual Explanations (XAI) [68.8204255655161]
  The emphasis of XAI research appears to have turned to a more pragmatic explanation approach for better understanding. An extensive area where cognitive science research may substantially influence XAI advancements is evaluating user knowledge and feedback. We propose a framework to experiment with generating and evaluating the explanations on the grounds of different cognitive levels of understanding.
  arXiv  Detail & Related papers  (2022-10-31T19:20:22Z)
• Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AI [65.44737844681256]
  A lack of consensus on how to evaluate explainable AI (XAI) hinders the advancement of the field. We argue that one way to close the gap is to develop evaluation methods that account for different user requirements.
  arXiv  Detail & Related papers  (2022-06-22T05:17:33Z)
• Investigating Explainability of Generative AI for Code through Scenario-based Design [44.44517254181818]
  generative AI (GenAI) technologies are maturing and being applied to application domains such as software engineering. We conduct 9 workshops with 43 software engineers in which real examples from state-of-the-art generative AI models were used to elicit users' explainability needs. Our work explores explainability needs for GenAI for code and demonstrates how human-centered approaches can drive the technical development of XAI in novel domains.
  arXiv  Detail & Related papers  (2022-02-10T08:52:39Z)
• Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunities [0.0]
  Explainable AI (XAI) has been proposed to make AI more transparent and thus advance the adoption of AI in critical domains. This study presents a systematic meta-survey for challenges and future research directions in XAI.
  arXiv  Detail & Related papers  (2021-11-11T19:06:13Z)
• A User-Centred Framework for Explainable Artificial Intelligence in Human-Robot Interaction [70.11080854486953]
  We propose a user-centred framework for XAI that focuses on its social-interactive aspect. The framework aims to provide a structure for interactive XAI solutions thought for non-expert users.
  arXiv  Detail & Related papers  (2021-09-27T09:56:23Z)
• Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey [2.7086321720578623]
  Black-box nature of deep neural networks challenges its use in mission critical applications. XAI promotes a set of tools, techniques, and algorithms that can generate high-quality interpretable, intuitive, human-understandable explanations of AI decisions.
  arXiv  Detail & Related papers  (2020-06-16T02:58:10Z)

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.