Search results diversification for effective fair ranking in academic search
-
Published:2021-12-07
Issue:1
Volume:25
Page:1-26
-
ISSN:1386-4564
-
Container-title:Information Retrieval Journal
-
language:en
-
Short-container-title:Inf Retrieval J
Author:
McDonald GrahamORCID, Macdonald CraigORCID, Ounis IadhORCID
Abstract
AbstractProviding users with relevant search results has been the primary focus of information retrieval research. However, focusing on relevance alone can lead to undesirable side effects. For example, small differences between the relevance scores of documents that are ranked by relevance alone can result in large differences in the exposure that the authors of relevant documents receive, i.e., the likelihood that the documents will be seen by searchers. Therefore, developing fair ranking techniques to try to ensure that search results are not dominated, for example, by certain information sources is of growing interest, to mitigate against such biases. In this work, we argue that generating fair rankings can be cast as a search results diversification problem across a number of assumed fairness groups, where groups can represent the demographics or other characteristics of information sources. In the context of academic search, as in the TREC Fair Ranking Track, which aims to be fair to unknown groups of authors, we evaluate three well-known search results diversification approaches from the literature to generate rankings that are fair to multiple assumed fairness groups, e.g. early-career researchers vs. highly-experienced authors. Our experiments on the 2019 and 2020 TREC datasets show that explicit search results diversification is a viable approach for generating effective rankings that are fair to information sources. In particular, we show that building on xQuAD diversification as a fairness component can result in a significant ($$p<0.05$$
p
<
0.05
) increase (up to 50% in our experiments) in the fairness of exposure that authors from unknown protected groups receive.
Publisher
Springer Science and Business Media LLC
Subject
Library and Information Sciences,Information Systems
Reference62 articles.
1. Abebe, R., Barocas, S., Kleinberg, J. M., Levy, K., Raghavan, M., & Robinson, D. G. (2020). Roles for computing in social change. In Proceedings of the FAT* Conference on Fairness, Accountability, and Transparency, Barcelona, Spain, January 27–30, 2020, ACM, pp. 252–260. https://doi.org/10.1145/3351095.3372871. 2. Agrawal, R., Gollapudi, S., Halverson, A., & Ieong, S. (2009). Diversifying search results. In Proceedings of the Second International Conference on Web Search and Web Data Mining, WSDM 2009, Barcelona, Spain, February 9–11, 2009, ACM, pp. 5–14. https://doi.org/10.1145/1498759.1498766. 3. Ammar, W., Groeneveld, D., Bhagavatula, C., Beltagy, I., Crawford, M., Downey, D., Dunkelberger, J., Elgohary, A., Feldman, S., Ha, V., Kinney, R., Kohlmeier, S., Lo, K., Murray, T., Ooi, H., Peter, M. E., Power, J., Skjonsberg, S., Wang, L. L., Wilhelm, C., Yuan, Z., van Zuylen, M., & Etzioni, O. (2018). Construction of the literature graph in semantic scholar. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2018, New Orleans, Louisiana, USA, June 1-6, 2018, Volume 3 (Industry Papers), Association for Computational Linguistics, pp. 84–91. https://doi.org/10.18653/v1/n18-3011. 4. Baeza-Yates, R. (2018). Bias on the web. Communication in ACM, 61(6), 54–61. https://doi.org/10.1145/3209581 5. Belkin, N. J., & Robertson, S. E. (1976). Some ethical implications of theoretical research in information science. InThe ASIS Annual Meeting.
Cited by
12 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|