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Proud of Vahid's use of #computational #CogSci to identify and compare #reasoning errors in #Reddit users and communities.

He's presenting it at an #AI + #decisionSci workshop at #CMU : www.cmu.edu/ai-sdm/r...

Follow him for alerts about this and more: www.researchgate.net...
Beyond Fact-Checking: Empowering Flexible Human-AI Teams to Detect & Counter Online Misinformation Vahid Ashrafi, Nick Byrd, and Jordan W. Suchow Misinformation is persistent: From pandemic conspiracies to election denial, false claims spread faster and farther than facts, threatening health and democracy. Current defenses fall short: Fact-checking and moderation focus on sentiment but overlook the cognitive vulnerabilities that make cognitive errors drive spread. Biases like confirmation bias, emotional reasoning, and exaggerated thinking distort judgment and amplify viral narratives. We detect and quantify 419 cognitive errors in Reddit users' text, building "cognitive fingerprints" that reveal reasoning flaws behind misinformation. Impact: These insights enable flexible human-AI teams to anticipate, detect, and counteract misinformation. Beyond Fact-Checking: Empowering Flexible Human-AI Teams to Detect and Counter Online Misinformation Vahid Ashrafi, Nick Byrd, and Jordan Suchow Abstract. [Vahid construced] a comprehensive knowledge base of 419 cognitive errors—including cognitive biases and logical fallacies—grounded in empirical findings from psychology and cognitive science. We then trained GPT-4.1, a state-of-the-art language model, to identify potential examples of these cognitive errors in users' posts. ... The robustness of AI-generated insights was rigorously validated by human annotators, achieving high reliability (Spearman’s ρ = 0.86). ... we propose actionable solutions such as targeted cognitive interventions to educate users about common reasoning errors, AI-driven moderation systems that identify problematic reasoning patterns rather than merely flagging false information, and personalized cognitive profiling tools to proactively identify at-risk individuals and communities. The NSF AI Institute for Societal Decision Making (NSF AI-SDM) sponsors the participation of selected speakers and students in an annual workshop of Human-AI Complementarity for Decision Making. Human-AI Complementarity, defined as the condition in which Humans + AI working together results in better decisions than humans or AI working alone, is a broad goal pursued in several projects of the NSF AI-SDM. In 2025, we will focus on how to create flexible Human-AI teams to achieve complementarity. This theme refers to the interdisciplinary study of how to design and deploy AI systems in ways that are dynamically aligned with human values, robust to unexpected behavior, and safe even under failure modes. It encompasses short-term concerns about deployed systems (e.g., fairness, robustness, interpretability, and misuse) and long-term concerns about advanced general AI (AGI) that could have large-scale societal impacts if not aligned with human interests.
September 26, 2025 at 4:53 PM