Querying with Conflicts of Interest

2026-03-05

AI Summary

This work addresses the problem of biased query responses arising from conflicts of interest between data sources and users, which can intentionally distort results and undermine users’ ability to access relevant information. The paper presents the first formal model of query bias under such strategic incentives and introduces a novel framework that integrates logical reasoning, efficient bias detection, and query rewriting to recover truthful and relevant information from biased responses. Experimental evaluation on large-scale real-world datasets demonstrates that the proposed approach is both computationally efficient and highly effective, significantly improving retrieval relevance. This study offers a principled and practical solution to counteract strategic information manipulation in adversarial data environments.

Technology Category

• Data Mining & Knowledge Management: Intelligent Query Processing
• Knowledge Representation and Reasoning: Preferences
• Search and Optimization: Adversarial Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

Abstract

Conflicts of interest often arise between data sources and their users regarding how the users'information needs should be interpreted by the data source. For example, an online product search might be biased towards presenting certain products higher than in its list of results to improve its revenue, which may not follow the user's desired ranking expressed in their query. The research community has proposed schemes for data systems to implement to ensure unbiased results. However, data systems and services usually have little or no incentive to implement these measures, e.g., these biases often increase their profits. In this paper, we propose a novel formal framework for querying in settings where the data source has incentives to return biased answers intentionally due to the conflict of interest between the user and the data source. We propose efficient algorithms to detect whether it is possible for users to extract relevant information from biased data sources. We propose methods to detect biased information in the results of a query efficiently. We also propose algorithms to reformulate input queries to increase the amount of relevant information in the returned results over biased data sources. Using experiments on real-world datasets, we show that our algorithms are efficient and return relevant information over large data.

Problem

• conflict of interest
• biased query results
• information retrieval
• query answering
• data source bias

Innovation

• conflict of interest
• biased query answering
• query reformulation
• bias detection
• information retrieval