The XAI-Seeking Principle: Structuring Explainability for LLM-Mediated Decision SupportWe investigate how LLM-mediated explanations can preserve evidence trails, support orientation, and reduce interpretation effort under cognitive and temporal constraints.
doi
10.1145/3800935.3830869
isbn
979-8-4007-2564-7
name
The XAI-Seeking Principle: Structuring Explainability for LLM-Mediated Decision Support
source
html
acm_url
https://dl.acm.org/doi/10.1145/3800935.3830869
authors
Valentin Grimm, Jessica Rubart, Eelco Herder, Carsten Röcker
doi_url
https://doi.org/10.1145/3800935.3830869
license
CC BY 4.0
summary
We investigate how LLM-mediated explanations can preserve evidence trails, support orientation, and reduce interpretation effort under cognitive and temporal constraints.
keywords
Large Language Model Mediation; Explainable AI; Decision Co-Pilot Systems; Misinformation Detection
published
2026-09-14
conference
HT '26: 37th ACM Conference on Hypertext, London, United Kingdom, September 14–18, 2026
acm_html_url
https://dl.acm.org/doi/fullHtml/10.1145/3800935.3830869
ccs_concepts
CCS Concepts: • Computing methodologies → Information extraction ; • Computing methodologies → Natural language generation ; • Human-centered computing → Empirical studies in interaction design ; • Information systems → Decision support systems ;
displayAuthor
Valentin Grimm, Jessica Rubart, Eelco Herder, Carsten Röcker
proceedings_url
https://dl.acm.org/doi/proceedings/10.1145/3800935
displayPublishTime
2026-09-14
acm_reference_format
ACM Reference Format: Valentin Grimm, Jessica Rubart, Eelco Herder, and Carsten Röcker. 2026. The XAI-Seeking Principle: Structuring Explainability for LLM-Mediated Decision Support. In 37th ACM Conference on Hypertext (HT '26), September 14--18, 2026, London, United Kingdom. ACM, New York, NY, USA 7 Pages. https://doi.org/10.1145/3800935.3830869