Annotator Risk Preference as a Catalyst for Systemic Bias in Multimodal AI

Shuofeng Hu, Zhen He, Tongtong Kan, Xiaomin Ying
Proceedings of AAAI 2026 Workshop on Bias in Multimodal AI, PMLR 332:15-18, 2026.

Abstract

As artificial intelligence evolves toward multimodal cognition, systems are moving beyond unimodal dependencies to integrate visual, auditory, and linguistic dimensions, thereby simulating human perception of reality. However, this increased complexity not only enhances expressiveness but also opens more insidious channels for bias infiltration. Existing research largely focuses on the demographic attributes of annotators (e.g., race, gender) while overlooking critical variables within the dimension of decision psychology (Ferrara, 2024; Sap et al., 2022). Among these, risk preference acts as a core driver of individual decision-making, exerting a subtle anchoring effect during the multimodal annotation process. When annotators confront materials characterized by high ambiguity, fuzziness, or potential social sensitivity, their intrinsic risk tolerance directly dictates label polarity, the degree of neutralization, and sensitivity toward minority attributes.

Cite this Paper

BibTeX
@InProceedings{pmlr-v332-hu26a, title = {Annotator Risk Preference as a Catalyst for Systemic Bias in Multimodal AI}, author = {Hu, Shuofeng and He, Zhen and Kan, Tongtong and Ying, Xiaomin}, booktitle = {Proceedings of AAAI 2026 Workshop on Bias in Multimodal AI}, pages = {15--18}, year = {2026}, editor = {Han, Soyeon Caren and Cabral, Rina Carines}, volume = {332}, series = {Proceedings of Machine Learning Research}, month = {25 Jan}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v332/main/assets/hu26a/hu26a.pdf}, url = {https://proceedings.mlr.press/v332/hu26a.html}, abstract = {As artificial intelligence evolves toward multimodal cognition, systems are moving beyond unimodal dependencies to integrate visual, auditory, and linguistic dimensions, thereby simulating human perception of reality. However, this increased complexity not only enhances expressiveness but also opens more insidious channels for bias infiltration. Existing research largely focuses on the demographic attributes of annotators (e.g., race, gender) while overlooking critical variables within the dimension of decision psychology (Ferrara, 2024; Sap et al., 2022). Among these, risk preference acts as a core driver of individual decision-making, exerting a subtle anchoring effect during the multimodal annotation process. When annotators confront materials characterized by high ambiguity, fuzziness, or potential social sensitivity, their intrinsic risk tolerance directly dictates label polarity, the degree of neutralization, and sensitivity toward minority attributes.} }
Endnote
%0 Conference Paper %T Annotator Risk Preference as a Catalyst for Systemic Bias in Multimodal AI %A Shuofeng Hu %A Zhen He %A Tongtong Kan %A Xiaomin Ying %B Proceedings of AAAI 2026 Workshop on Bias in Multimodal AI %C Proceedings of Machine Learning Research %D 2026 %E Soyeon Caren Han %E Rina Carines Cabral %F pmlr-v332-hu26a %I PMLR %P 15--18 %U https://proceedings.mlr.press/v332/hu26a.html %V 332 %X As artificial intelligence evolves toward multimodal cognition, systems are moving beyond unimodal dependencies to integrate visual, auditory, and linguistic dimensions, thereby simulating human perception of reality. However, this increased complexity not only enhances expressiveness but also opens more insidious channels for bias infiltration. Existing research largely focuses on the demographic attributes of annotators (e.g., race, gender) while overlooking critical variables within the dimension of decision psychology (Ferrara, 2024; Sap et al., 2022). Among these, risk preference acts as a core driver of individual decision-making, exerting a subtle anchoring effect during the multimodal annotation process. When annotators confront materials characterized by high ambiguity, fuzziness, or potential social sensitivity, their intrinsic risk tolerance directly dictates label polarity, the degree of neutralization, and sensitivity toward minority attributes.
APA
Hu, S., He, Z., Kan, T. & Ying, X.. (2026). Annotator Risk Preference as a Catalyst for Systemic Bias in Multimodal AI. Proceedings of AAAI 2026 Workshop on Bias in Multimodal AI, in Proceedings of Machine Learning Research 332:15-18 Available from https://proceedings.mlr.press/v332/hu26a.html.

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