SIMULATION OF COGNITIVE BIASES: UNDERSTANDING DECISION-MAKING IN HUMAN-AI INTERACTIONS

Authors

  • JOHN MARK PANTOÑAL Author

Keywords:

cognitive biases, human-AI interactions, decision-making, bias mitigation, AI fairness, computational modeling

Abstract

Cognitive biases refer to systematic patterns of deviation from rationality in judgment, often influencing human decision-making in ways that may lead to suboptimal or flawed outcomes. This paper explores the impact of cognitive biases in the context of human-AI interactions, particularly in decision-making scenarios. Using simulation models, the study investigates various cognitive biases, such as confirmation bias, anchoring, and the availability heuristic, and their effects when humans interact with AI systems. The research highlights how biases can be amplified or mitigated by AI, and examines the potential for AI to inadvertently reinforce biased decisions. Findings indicate that while AI can assist in reducing certain biases, it may also unintentionally reproduce human cognitive errors unless properly designed. The paper provides insights into the design of AI systems that account for cognitive biases and improve the quality of decision-making processes in diverse fields, such as healthcare, finance, and criminal justice.

 

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Published

2024-12-28

How to Cite

SIMULATION OF COGNITIVE BIASES: UNDERSTANDING DECISION-MAKING IN HUMAN-AI INTERACTIONS. (2024). Cognify : Journal of Artificial Intelligence and Cognitive Science, 1(4), 77-85. https://researchvision.us/index.php/cognify/article/view/154

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