Year: 2025
Type: article
Abstract: Information retrieval systems aim to return relevant and useful content to users and are often biased towards popular items. This implies that an under-represented group or attribute will not receive a fair share of a user’s attention in search results. For example, while a ranked results list for a query such as ‘physicists’ might be fair according to a particular attribute such as gender, nationality or social group, it might not be fair for all of them. Ideally, while providing relevant answers, a results list should also provide fair exposure across a broad range of attributes. We demonstrate that while a system can be fair towards multiple attributes, they are not necessarily diverse (i.e., redundancy/minimal novelty). To this end, we include an additional dimension to the study, i.e., diversity, and explore the relationship between fairness and diversity measures by exploring popular search result diversification techniques using the test collections from TREC 2021 Fair Ranking Track, TREC 2022 Fair Ranking Track and NTCIR-17 FairWeb-1. Furthermore, we study the impact of such diversification techniques along both nominal and ordinal attributes, as well as for intersectional fairness. Our results indicate that explicit search results diversification techniques showed improved results when the attributes were nominal but failed to provide fairer and more diverse results when the attributes were ordinal in nature. Additionally, in terms of intersectional fairness explicit search results diversification also performed significantly better than baseline retrieval runs.
DOI: 10.1145/3774320
Institution: RMIT University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Reduced inequalities
Open Access status: bronze
Funder: Australian Research Council
OpenAlex ID: W4415736123