activity
20222026
most citedHeterogeneous Graph Neural Networks with Post-hoc Explanations for Multi-modal and Explainable Land Use Inference

11 citations · 36 across the 18 of their papers we have counts for

collaborators
Showing cs.AIShow all

5 papers · 1 filter

cs.AI2026

A Theory of Post-hoc Debate Judgement

Xiang Yin, Adam Dejl, Antonio Rago +2

Debates have recently emerged as a useful methodology for agentic AI to improve performance as well as to aid explainability and user engagement. For example, LLM-empowered agents…

cs.AI2026

Neurosymbolic Learning for Inference-Time Argumentation

Gabriel Freedman, Adam Dejl, Adam Gould +4

Claim verification is an important problem in high-stakes settings, including health and finance. When information underpinning claims is incomplete or conflicting, uncertain answe…

cs.AI2026

Argumentation for Explainable and Globally Contestable Decision Support with LLMs

Adam Dejl, Matthew Williams, Francesca Toni

Large language models (LLMs) exhibit strong general capabilities, but their deployment in high-stakes domains is hindered by their opacity and unpredictability. Recent work has tak…

cs.AI2024★ 11 cited

Heterogeneous Graph Neural Networks with Post-hoc Explanations for Multi-modal and Explainable Land Use Inference

Xuehao Zhai, Junqi Jiang, Adam Dejl +4

Urban land use inference is a critically important task that aids in city planning and policy-making. Recently, the increased use of sensor and location technologies has facilitate…

cs.AI2024★ 6 cited

Contestable AI needs Computational Argumentation

Francesco Leofante, Hamed Ayoobi, Adam Dejl +10

AI has become pervasive in recent years, but state-of-the-art approaches predominantly neglect the need for AI systems to be contestable. Instead, contestability is advocated by AI…