Tootfinder

You probably know I get frowny when browser makers lie about support or pretend their late implementation of a feature is somehow new, so I am suffering from a bunch of confirmation bias (and validation) here:
“Safari at WWDC '25: The Ghost of Christmas Past”
https://infrequently.org/2025…

Safari at WWDC '25: The Ghost of Christmas Past
Alex Russell on browsers, standards, and the process of progress.

Exploring Content and Social Connections of Fake News with Explainable Text and Graph Learning
V\'itor N. Louren\c{c}o, Aline Paes, and Tillman Weyde
https://arxiv.org/abs/2508.10040

Exploring Content and Social Connections of Fake News with Explainable Text and Graph Learning
The global spread of misinformation and concerns about content trustworthiness have driven the development of automated fact-checking systems. Since false information often exploits social media dynamics such as "likes" and user networks to amplify its reach, effective solutions must go beyond content analysis to incorporate these factors. Moreover, simply labelling content as false can be ineffective or even reinforce biases such as automation and confirmation bias. This paper proposes an expl…

This https://arxiv.org/abs/2505.10661 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

It's only fair when I think it's fair: How Gender Bias Alignment Undermines Distributive Fairness in Human-AI Collaboration
Human-AI collaboration is increasingly relevant in consequential areas where AI recommendations support human discretion. However, human-AI teams' effectiveness, capability, and fairness highly depend on human perceptions of AI. Positive fairness perceptions have been shown to foster trust and acceptance of AI recommendations. Yet, work on confirmation bias highlights that humans selectively adhere to AI recommendations that align with their expectations and beliefs -- despite not being necessa…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[1/5]:
- Combating Confirmation Bias: A Unified Pseudo-Labeling Framework for Entity Alignment
Qijie Ding, Jie Yin, Daokun Zhang, Junbin Gao

I feel very undecided about the book I picked up: “Think Like a Monk” by Jay Shetty.
There’s a nagging thought in the back of my mind that the book might not be what I’m looking for. The confirmation bias kicked in immediately and within minutes it called out the following: “Three years after I moved to Mumbai, my teacher, Gauranga Das, told me he believed I would be of greater value and service if I left the ashram and shared what I’d learned with the world”. So these are the teachings of someone who studied for 3 years what others are practicing a life time and then was sent out to be a marketer.

This https://arxiv.org/abs/2412.04629 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Argumentative Experience: Reducing Confirmation Bias on Controversial Issues through LLM-Generated Multi-Persona Debates
Large language models (LLMs) are enabling designers to give life to exciting new user experiences for information access. In this work, we present a system that generates LLM personas to debate a topic of interest from different perspectives. How might information seekers use and benefit from such a system? Can centering information access around diverse viewpoints help to mitigate thorny challenges like confirmation bias in which information seekers over-trust search results matching existing …

Navigating the Lobbying Landscape: Insights from Opinion Dynamics Models
Daniele Giachini, Leonardo Ciambezi Verdiana Del Rosso, Fabrizio Fornari, Valentina Pansanella, Lilit Popoyan, Alina S\^irbu
https://arxiv.org/abs/2507.13767

Navigating the Lobbying Landscape: Insights from Opinion Dynamics Models
While lobbying has been demonstrated to have an important effect on public opinion and policy making, existing models of opinion formation do not specifically include its effect. In this work we introduce a new model of opinion dynamics where lobbyists can implement complex strategies and are characterised by a finite budget. Individuals update their opinions through a learning process resembling Bayesian learning, but influenced by cognitive biases such as under-reaction and confirmation bias.…