Responsible AI Research Lab at The University of Queensland, Australia
Building AI for public good.
DLab is an interdisciplinary research group exploring how data, people, and AI affect each other.
Focus
Responsible AI and sociotechnical design
Approach
Interdisciplinary collaboration with real-world partners
Community
Researchers, students, and institutions shaping the future of AI
Perspective-Aware LLM Evaluation
We analyze how large language models encode political and demographic perspectives, with a focus on bias, relevance judgments, and summarization effects.
Human-Centered Data Quality
Our research combines metadata, crowdsourcing, and information retrieval methods to improve quality assessment and trust in unstructured data workflows.
Social Media Integrity and Safety
We study misinformation, harmful content, and online persuasion, developing methods and tools for fairer and more transparent sociotechnical systems.
Latest Publications
The 5 most recent entries from our publications page, highlighting current work across LLMs, fairness, relevance, and social media analysis.
Stefano Civelli, Pietro Bernardelle, Nicolò Brunello, and Gianluca Demartini. A Shared Geometry of Difficulty in Multilingual Language Models. In: The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) - Short paper. San Diego, California, July 2026.
Stefano Civelli, Pietro Bernardelle, Nardiena A. Pratama, and Gianluca Demartini. Ideology-Based LLMs for Content Moderation. In: ACM Transactions on Intelligent Systems and Technology (TIST). 2026.
Pietro Bernardelle, Leon Fröhling, Stefano Civelli, and Gianluca Demartini. SubData: Bridging Heterogeneous Datasets to Enable Theory-Driven Evaluation of Political and Demographic Perspectives in LLMs. In: 5th Workshop on Perspectivist Approaches to NLP (NLPerspectives 2026) at the 15th Language Resources and Evaluation Conference (LREC). Mallorca, Spain, May 2026.
Amit Arjun Verma, Simran Setia, and Gianluca Demartini. Scalable Methods for Storing and Retrieving Wikipedia Revision Histories for Large-Scale Analysis. In: ACM Transactions on the Web (TWEB). 2026.
Samaneh Mohtadi, and Gianluca Demartini. Query-Document Dense Vectors for LLM Relevance Judgment Bias Analysis. In: The 48th European Conference on Information Retrieval (ECIR 2026) - Findings. Delft, The Netherlands, March 2026.