mlsanityData to 14 Oct 2026

Exploring Conversational Agents as an Effective Tool for Measuring Cognitive Biases in Decision-Making

arXiv 2401.06686 ↗First posted 8 Jan 2024cs.HC cs.AIcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 6 of its 28 references that are in the map. First posted on arXiv 8 Jan 2024.

Builds on

6 of 28 references.

References · 6

  1. 2021Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Surveycited by 130
  2. 2017Frames: A Corpus for Adding Memory to Goal-Oriented Dialogue Systemscited by 85
  3. 2023Leveraging Large Language Models to Power Chatbots for Collecting User Self-Reported Datacited by 48
  4. 2019A Survey of Natural Language Generation Techniques with a Focus on Dialogue Systems - Past, Present and Future Directionscited by 26
  5. 2020Stylized Dialogue Response Generation Using Stylized Unpaired Textscited by 16
  6. 2022BIASeD: Bringing Irrationality into Automated System Designcited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 3.

4 papers.

  1. 2024A Comprehensive Evaluation of Cognitive Biases in LLMscited by 34
  2. 2026DERELAB: Probing Defeasible Reasoning and Confirmation Bias in LLMs with a Generative Benchmarkcited by 1
  3. 2026Predicting Biased Human Decision-Making with Large Language Models in Conversational Settingscited by 0
  4. 2026Emulating Aggregate Human Choice Behavior and Biases with GPT Conversational Agentscited by 0