Tootfinder

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 …

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.

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.

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…