Interactive Interventions to Mitigate Cognitive Bias.

User Modeling, Adaptation, and Personalization (UMAP)(2022)

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摘要
While the web offers a great potential to find and share information, the cognitively demanding conditions of online interactions can leave users vulnerable to cognitive biases, such as the confirmation bias - the tendency to favor information that confirms prior attitudes and beliefs when searching for, selecting, interpreting, sharing, and recalling information. This can negatively impact individuals' decision-making and is likely to drive ideological polarization and extremism. With my dissertation, I am investigating whether and how interactive bias mitigation interventions, with a special focus on confirmation bias, could empower web users in making informed, unbiased, and autonomous choices. Based on my findings and observations, I plan to build a framework of user- and context-adaptive bias mitigation approaches during different kinds of web interactions.
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