TrustME: A Context-Aware Explainability Model to Promote User Trust in Guidance

Information

• Publication Type: Journal Paper (without talk)
• Workgroup(s)/Project(s): not specified
• Date: April 2025
• DOI: 10.1109/TVCG.2025.3562929
• ISSN: 1941-0506
• Journal: IEEE Transactions on Visualization and Computer Graphics
• Pages: 17
• Publisher: IEEE COMPUTER SOC
• Keywords: Explainability, Explainable Guidance, User Trust, Visual Analytics

Abstract

Guidance-enhanced approaches are used to support users in making sense of their data and overcoming challenging analytical scenarios. While recent literature underscores the value of guidance, a lack of clear explanations to motivate system interventions may still negatively impact guidance effectiveness. Hence, guidance-enhanced VA approaches require meticulous design, demanding contextual adjustments for developing appropriate explanations. Our paper discusses the concept of explainable guidance and how it impacts the user-system relationship-specifically, a user's trust in guidance within the VA process. We subsequently propose a model that supports the design of explainability strategies for guidance in VA. The model builds upon flourishing literature in explainable AI, available guidelines for developing effective guidance in VA systems, and accrued knowledge on user-system trust dynamics. Our model responds to challenges concerning guidance adoption and context-effectiveness by fostering trust through appropriately designed explanations. To demonstrate the model's value, we employ it in designing explanations within two existing VA scenarios. We also describe a design walk-through with a guidance expert to showcase how our model supports designers in clarifying the rationale behind system interventions and designing explainable guidance.

Additional Files and Images

Weblinks

• Entry in reposiTUm (TU Wien Publication Database)
• DOI: 10.1109/TVCG.2025.3562929

BibTeX

@article{musleh-2025-trustme,
  title =      "TrustME: A Context-Aware Explainability Model to Promote
               User Trust in Guidance",
  author =     "Maath Musleh and Renata Raidou and Davide Ceneda",
  year =       "2025",
  abstract =   "Guidance-enhanced approaches are used to support users in
               making sense of their data and overcoming challenging
               analytical scenarios. While recent literature underscores
               the value of guidance, a lack of clear explanations to
               motivate system interventions may still negatively impact
               guidance effectiveness. Hence, guidance-enhanced VA
               approaches require meticulous design, demanding contextual
               adjustments for developing appropriate explanations. Our
               paper discusses the concept of explainable guidance and how
               it impacts the user-system relationship-specifically, a
               user's trust in guidance within the VA process. We
               subsequently propose a model that supports the design of
               explainability strategies for guidance in VA. The model
               builds upon flourishing literature in explainable AI,
               available guidelines for developing effective guidance in VA
               systems, and accrued knowledge on user-system trust
               dynamics. Our model responds to challenges concerning
               guidance adoption and context-effectiveness by fostering
               trust through appropriately designed explanations. To
               demonstrate the model's value, we employ it in designing
               explanations within two existing VA scenarios. We also
               describe a design walk-through with a guidance expert to
               showcase how our model supports designers in clarifying the
               rationale behind system interventions and designing
               explainable guidance.",
  month =      apr,
  doi =        "10.1109/TVCG.2025.3562929",
  issn =       "1941-0506",
  journal =    "IEEE Transactions on Visualization and Computer Graphics",
  pages =      "17",
  publisher =  "IEEE COMPUTER SOC",
  keywords =   "Explainability, Explainable Guidance, User Trust, Visual
               Analytics",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2025/musleh-2025-trustme/",
}