How can we build trust in an era of bots and lies?
Botwatch
Fight bots by warning others. Support humans with verification. Build communities with trust.
Proposing Botwatch: Trust-Based Community Bot Detection
Is this account a bot? This question’s importance is only growing in a world of LLMs and state-backed influence campaigns. Proposed here is Botwatch, a community-based system for personalized bot detection. In Botwatch, users publish records indicating whether they think others are bots and records indicating trust in a user’s scores. By analyzing this network, we can create useful signals to help users distinguish between bots and humans. Such a signal would consider your trust relations and o...
AI chatbots are becoming experts at changing people's minds. What's their secret?
ChatGPT and other AIs use a flood of facts, and the occasional lie, to persuade humans
Proton survey: People don't trust AI, but they tell it everything | Proton
New research finds that most AI chatbot users have shared something sensitive, but few trust the companies behind them.
Moderating With Humans, For Humans - Trust Issues
Moderating With Humans, For Humans - Trust Issues
Unauthorised AI Bots on Reddit are Eroding Sociality
Where do we go if online discourse is overrun by AI bots?
Exposure to social bots amplifies perceptual biases and regulation propensity
Automated accounts on social media that impersonate real users, often called “social bots,” have received a great deal of attention from academia and the public. Here we present experiments designed to investigate public perceptions and policy preferences about social bots, in particular how they are affected by exposure to bots. We find that before exposure, participants have some biases: they tend to overestimate the prevalence of bots and see others as more vulnerable to bot influence than themselves. These biases are amplified after bot exposure. Furthermore, exposure tends to impair judgment of bot-recognition self-efficacy and increase propensity toward stricter bot-regulation policies among participants. Decreased self-efficacy and increased perceptions of bot influence on others are significantly associated with these policy preference changes. We discuss the relationship between perceptions about social bots and growing dissatisfaction with the polluted social media environment.
Exposure to social bots amplifies perceptual biases and regulation propensity
Automated accounts on social media that impersonate real users, often called “social bots,” have received a great deal of attention from academia and the public. Here we present experiments designed to investigate public perceptions and policy preferences about social bots, in particular how they are affected by exposure to bots. We find that before exposure, participants have some biases: they tend to overestimate the prevalence of bots and see others as more vulnerable to bot influence than themselves. These biases are amplified after bot exposure. Furthermore, exposure tends to impair judgment of bot-recognition self-efficacy and increase propensity toward stricter bot-regulation policies among participants. Decreased self-efficacy and increased perceptions of bot influence on others are significantly associated with these policy preference changes. We discuss the relationship between perceptions about social bots and growing dissatisfaction with the polluted social media environment.
Exposure to social bots amplifies perceptual biases and regulation propensity
Automated accounts on social media that impersonate real users, often called “social bots,” have received a great deal of attention from academia and the public. Here we present experiments designed to investigate public perceptions and policy preferences about social bots, in particular how they are affected by exposure to bots. We find that before exposure, participants have some biases: they tend to overestimate the prevalence of bots and see others as more vulnerable to bot influence than themselves. These biases are amplified after bot exposure. Furthermore, exposure tends to impair judgment of bot-recognition self-efficacy and increase propensity toward stricter bot-regulation policies among participants. Decreased self-efficacy and increased perceptions of bot influence on others are significantly associated with these policy preference changes. We discuss the relationship between perceptions about social bots and growing dissatisfaction with the polluted social media environment.
Trust: Foundation of Human Knowledge yet online Platforms won't allow | David Karger | TEDxMIT Salon
Commence the Botwatch | Botwatch Blog
Bots were already a pain online when they were little more than if-then scripts. Now a tidal wave of slop is swamping the internet as big tech companies profit. OpenAI and the rest would love for you to believe they care about "alignment" and "values" while their AI models damage our communities, both online and off. It's time to fight back.
LinkedTrust — Deep Tech. Human Trust.
Verified trust systems, civic tech platforms, and AI-powered tools. Real projects, real impact.
AI Tools for Trust: Community Notes, Rhetoric Detection & More
Five AI technologies to combat misinformation: community notes, rhetoric detection, reliability tracking, epistemic evals, and provenance tracing.
Deepfakes, Scams, and the Age of Paranoia
As AI-driven fraud becomes increasingly common, more people feel the need to verify every interaction they have online.
