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Exploring Conversational Agents as an Effective Tool for Measuring Cognitive Biases in Decision-Making (2401.06686v1)

Published 8 Jan 2024 in cs.HC and cs.AI

Abstract: Heuristics and cognitive biases are an integral part of human decision-making. Automatically detecting a particular cognitive bias could enable intelligent tools to provide better decision-support. Detecting the presence of a cognitive bias currently requires a hand-crafted experiment and human interpretation. Our research aims to explore conversational agents as an effective tool to measure various cognitive biases in different domains. Our proposed conversational agent incorporates a bias measurement mechanism that is informed by the existing experimental designs and various experimental tasks identified in the literature. Our initial experiments to measure framing and loss-aversion biases indicate that the conversational agents can be effectively used to measure the biases.

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Authors (1)
  1. Stephen Pilli (2 papers)
Citations (2)
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