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  4. Beyond Isolation: Towards an Interactionist Perspective on Human Cognitive Bias and AI Bias
 
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Beyond Isolation: Towards an Interactionist Perspective on Human Cognitive Bias and AI Bias

Journal
CHI 2025: Tools for Thought: Research and Design for Understanding, Protecting, and Augmenting Human Cognition with Generative AI
Type
conference contribution
Date Issued
2025-04-26
Author(s)
Nick von Felten
DOI
10.48550/arXiv.2504.18759
Abstract
Isolated perspectives have often paved the way for great scientific discoveries. However, many breakthroughs only emerged when moving away from singular views towards interactions. Discussions on Artificial Intelligence (AI) typically treat human and AI bias as distinct challenges, leaving their dynamic interplay and compounding potential largely unexplored. Recent research suggests that biased AI can amplify human cognitive biases, while well-calibrated systems might help mitigate them. In this position paper, I advocate for transcending beyond separate treatment of human and AI biases and instead focus on their interaction effects. I argue that a comprehensive framework, one that maps (compound human-AI) biases to mitigation strategies, is essential for understanding and protecting human cognition, and I outline concrete steps for its development. CCS CONCEPTS • Human-centered computing → HCI theory, concepts and models.
Language
English
Keywords
Compound Human-AI Bias
Artificial Intelligence
Human-centered Artificial Intelligence
Bias
Cognitive Bias
Algorithmic Bias
AI Bias
Human Bias De-biasing
Bias Mitigation
Human-Computer Interaction
Cognitive Psychology
Publisher
Association for Computing Machinery
Publisher place
New York, NY, USA
Official URL
https://arxiv.org/abs/2504.18759
URL
https://www.alexandria.unisg.ch/handle/20.500.14171/122685
Subject(s)

computer science

behavioral science

Division(s)

ICS - Institute of Co...

Contact Email Address
nick.vonfelten@unisg.ch
File(s)
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Thumbnail Image

open.access

Name

2504.18759v1 (1).pdf

Size

119.96 KB

Format

Adobe PDF

Checksum (MD5)

a067ccc28de4ec301fbea67fe033c283

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