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- W4387421125 abstract "It is well-known that cognitive bias influences information retrievers to receive information fairly. Among all the biases, we focus on confirmation bias, which is the most impactful and sometimes leads to polarization. Numerous projects have been working on improving search systems to provide fair results, while little work has been done on enhancing fairness from the user’s side. Thus, this project aims to investigate a quantifiable approach to make the systems aware that retrievers carry confirmation bias and perform biased behaviors. Besides, some works have attempted to detect confirmation bias using web-logging or eye-tracking but failed to find differences between the search behaviors. In this regard, other quantifiable and objective measures should be applied. This project aims to collect multi-modal behavioral and physiological data using wearable sensors, and used as the input for machine learning techniques and build a bias-aware model." @default.
- W4387421125 created "2023-10-08" @default.
- W4387421125 creator A5079572245 @default.
- W4387421125 date "2023-10-08" @default.
- W4387421125 modified "2023-10-18" @default.
- W4387421125 title "Quantifying and Measuring Confirmation Bias in Information Retrieval Using Sensors" @default.
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- W4387421125 doi "https://doi.org/10.1145/3594739.3610765" @default.
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