Perception-Aware Bias Detection for Query Suggestions

2026-01-07

International Workshop on Algorithmic Bias in Search and Recommendation

AI Summary

This work addresses the challenge of effectively detecting systemic thematic bias in query suggestion systems, which is hindered by data sparsity, insufficient contextual metadata, and the transient nature of user perception. Building upon the bias detection framework introduced by Bonart et al., the authors propose a novel “perception-aware” bias metric grounded in principles from perceptual psychology, specifically tailored for person-centric search scenarios. By explicitly modeling biases that are actually noticeable to users, the proposed approach substantially enhances the real-world relevance of bias detection outcomes. Experimental validation demonstrates that this refined pipeline more accurately identifies systemically embedded biases that users can perceive, thereby improving both the practical utility and interpretability of bias assessments in search systems.

Technology Category

• Computer Vision: Bias, Fairness & Privacy
• Search and Optimization: Evaluation and Analysis
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Social Networks and Social Media: Fairness and bias in social network and social media analysis

Abstract

Bias in web search has been in the spotlight of bias detection research for quite a while. At the same time, little attention has been paid to query suggestions in this regard. Awareness of the problem of biased query suggestions has been raised. Likewise, there is a rising need for automatic bias detection approaches. This paper adds on the bias detection pipeline for bias detection in query suggestions of person-related search developed by Bonart et al. \cite{Bonart_2019a}. The sparseness and lack of contextual metadata of query suggestions make them a difficult subject for bias detection. Furthermore, query suggestions are perceived very briefly and subliminally. To overcome these issues, perception-aware metrics are introduced. Consequently, the enhanced pipeline is able to better detect systematic topical bias in search engine query suggestions for person-related searches. The results of an analysis performed with the developed pipeline confirm this assumption. Due to the perception-aware bias detection metrics, findings produced by the pipeline can be assumed to reflect bias that users would discern.

Problem

• bias detection
• query suggestions
• perception-aware
• systematic topical bias
• person-related search

Innovation

• perception-aware bias detection
• query suggestions
• systematic topical bias
• person-related search
• bias detection pipeline