5 papers
Explaining Sources of Uncertainty in Automated Fact-Checking
Jingyi Sun, Greta Warren, Irina Shklovski +1
Understanding sources of a model's uncertainty regarding its predictions is crucial for effective human-AI collaboration. Prior work proposes using numerical uncertainty or hedges…
Show me the evidence: Evaluating the role of evidence and natural language explanations in AI-supported fact-checking
Greta Warren, Jingyi Sun, Irina Shklovski +1
Although much research has focused on AI explanations to support decisions in complex information-seeking tasks such as fact-checking, the role of evidence is surprisingly under-re…
Public Perceptions of Fairness Metrics Across Borders
Yuya Sasaki, Sohei Tokuno, Haruka Maeda +6
Which fairness metrics are appropriately applicable in your contexts? There may be instances of discordance regarding the perception of fairness, even when the outcomes comply with…
"Why do we do this?": Moral Stress and the Affective Experience of Ethics in Practice
Sonja Rattay, Ville Vakkuri, Marco Rozendaal +1
A plethora of toolkits, checklists, and workshops have been developed to bridge the well-documented gap between AI ethics principles and practice. Yet little is known about effects…
Show Me the Work: Fact-Checkers' Requirements for Explainable Automated Fact-Checking
Greta Warren, Irina Shklovski, Isabelle Augenstein
The pervasiveness of large language models and generative AI in online media has amplified the need for effective automated fact-checking to assist fact-checkers in tackling the in…