FAIRS: a framework for rethinking algorithmic fairness in the context of information access
DOI:
https://doi.org/10.47989/ir31262019Keywords:
Algorithmic fairness, Information access, Fairness framework, Algorithmic bias, Artificial intelligence, Machine learningAbstract
Introduction. Artificial intelligence and machine learning increasingly shape information access, enhancing efficiency but also amplifying biases that affect equity in access, exposure, and opportunity. This study introduces the FAIRS (‘Fair Algorithms for Information Retrieval and Seeking’) framework to address the pressing need for a comprehensive conceptualisation of algorithmic fairness in the context of information access.
Method. We build on a PRISMA-based literature review on scholarship relevant to algorithmic fairness and information access published between 2015 and 2025. The intersecting themes from over 100 peer-reviewed articles were triangulated to develop a conceptual framework.
Analysis. Thematic synthesis iteratively mapped themes connecting algorithmic fairness and information access across user roles, technologies, fairness issues, dimensions, solutions, metrics, bias, and contextual factors. These themes form the foundation of the FAIRS conceptual design.
Results. Existing research focuses on fairness in classification and rank-based personalisation. We determined that a comprehensive model of algorithmic fairness in information access must integrate metrics, context, barriers, technology, bias, fairness dimensions, and user perspectives—core components reflected in FAIRS.
Conclusions. FAIRS offers a novel, context-sensitive approach for defining, assessing, and operationalising fairness in information access. It provides a foundation for new fairness models, clarifies trade-offs, and supports the creation of holistic, equitable, and ethically grounded information systems.
References
Ai, Q., Wang, X., & Bendersky, M. (2023, July). Metric-agnostic ranking optimization. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 2669-2680). https://doi.org/10.1145/3539618.3591935
Akter, T., Ahmed, T., Kapadia, A., & Swaminathan, S. M. (2020, October). Privacy considerations of the visually impaired with camera based assistive technologies: Misrepresentation, impropriety, and fairness. In Proceedings of the 22nd International ACM SIGACCESS Conference on Computers and Accessibility (pp. 1-14). https://doi.org/10.1145/3373625.3417003
Alstyne, M. V., & Brynjolfsson, E. (2005). Global village or cyber-Balkans? Modeling and measuring the integration of electronic communities. Management Science, 51(6) (June 2005), 851–868. https://doi.org/10.1287/mnsc.1050.0363
Balagopalan, A., Jacobs, A. Z., & Biega, A. J. (2023, July). The role of relevance in fair ranking. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 2650-2660). https://doi.org/10.1145/3539618.3591933
Bains, J. (2020). Search engines and algorithms are biased. Here's why that matters: Librarians can take steps to address inequality and bias that result from using data historically weighted in favor of white men. Information Outlook, 24(1). https://scholarworks.sjsu.edu/sla_io_2020/1/ (Archived at https://web.archive.org/web/20240709201850/https://scholarworks.sjsu.edu/sla_io_2020/1/)
Barocas, S., & Selbst, A. D. (2016). Big data’s disparate impact. California Law Review, 104(3), 671-732. http://dx.doi.org/10.15779/Z38BG31
Bauer, M. W., & Schiele, B. (Eds.). (2024). AI and common sense: Ambitions and frictions. Taylor & Francis.
Beer, D. (2009). Power through the algorithm? Participatory web cultures and the technological unconscious. New Media and Society, 11(6), 985–1002. https://doi.org/10.1177/1461444809336551
Beer, D. (2013). Algorithms: Shaping tastes and manipulating the Ccrculations of popular culture. In Popular culture and new media: The politics of circulation (pp. 63–100). Palgrave Macmillan. https://doi.org/10.1057/9781137270061_4
Bernard, N., & Balog, K. (2025). A systematic review of fairness, accountability, transparency, and ethics in information retrieval. ACM Computing Surveys, 57(6). https://doi.org/10.1145/3637211
Blodgett, S. L., Barocas, S., Daumé III, H., & Wallach, H. (2020). Language (technology) is power: A critical survey of "bias" in NLP. arXiv. https://doi.org/10.48550/arXiv.2005.14050
Bolukbasi, T., Chang, K.-W., Zou, J. Y., Saligrama, V., & Kalai, A. T. (2016). Man is to computer programmer as woman is to homemaker? Debiasing word embeddings. In D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, & R. Garnett (Eds.), Advances in neural information processing systems (Vol. 29). Curran Associates, Inc.
Burke, R. (2017, July). Multisided fairness for recommendation. arXiv. https://doi.org/10.48550/arXiv.1707.00093Cachel, K., & Rundensteiner, E. (2024, October). Wise fusion: Group fairness enhanced rank fusion. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (pp. 163-174). https://doi.org/10.1145/3627673.3679649
Celis, L. E., & Keswani, V. (2020). Implicit diversity in image summarization. Proceedings of the ACM on Human-Computer Interaction, 4(CSCW2), 1-28. https://doi.org/10.1145/3415210
Chaney, A. J. B., Stewart, B. M., & Engelhardt, B. E. (2018). How algorithmic confounding in recommendation systems increases homogeneity and decreases utility. In Proceedings of the 12th ACM Conference on Recommender Systems (pp. 224–232). Association for Computing Machinery. https://doi.org/10.1145/3240323.3240370
Chavula, C., & Suleman, H. (2016, December). Assessing the impact of vocabulary similarity on multilingual information retrieval for Bantu languages. In Proceedings of the 8th Annual Meeting of the Forum for Information Retrieval Evaluation (pp. 16-23). https://doi.org/10.1145/3015157.3015160
Conn, J. (2010). Still stuck on neutrality. No legislative remedy likely over Internet fair play. Modern Healthcare, 40(41), 30-31. https://pubmed.ncbi.nlm.nih.gov/21033002/ (Archived at https://web.archive.org/web/20260422193955/https://pubmed.ncbi.nlm.nih.gov/21033002/)
Cornacchia, G., Anelli, V. W., Biancofiore, G. M., Narducci, F., Pomo, C., Ragone, A., & Di Sciascio, E. (2023). Auditing fairness under unawareness through counterfactual reasoning. Information Processing & Management, 60(2), 103224. https://doi.org/10.1016/j.ipm.2022.103224
Costa, N., Nielsen, M., Jull, G., Claus, A. P., & Hodges, P. W. (2020). Low back pain websites do not meet the needs of consumers: A study of online resources at three time points. Health Information Management Journal, 49(2-3), 137-149. https://doi.org/10.1177/1833358319857354
Crawford, K. (2016). Can an algorithm be agonistic? Ten scenes from life in calculated publics. Science, Technology & Human Values, 41(1), 77-92. https://doi.org/10.1177/0162243915589635
Crawford, K. (2017, December). The trouble with bias [Video]. YouTube. https://youtu.be/fMym_BKWQzk (Archived at https://web.archive.org/web/20251220102224/https://www.youtube.com/watch?v=fMym_BKWQzk)
Danaher, J. (2016). The threat of algocracy: Reality, resistance and accommodation. Philosophy & Technology, 29(3), 245–268. https://doi.org/10.1007/s13347-015-0211-1
Dash, A., Chakraborty, A., Ghosh, S., Mukherjee, A., & Gummadi, K. P. (2022). Alexa, in you, I trust! Fairness and interpretability issues in e-commerce search through smart speakers. In Proceedings of the ACM Web Conference 2022 (pp. 3695–3705). Association for Computing Machinery. https://doi.org/10.1145/3485447.3512265
Dhaliwal, M. P., Muskaan, & Seth, A. (2021). Fairness and diversity in the recommendation and ranking of participatory media content. In Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (pp. 43–47). Association for Computing Machinery. https://doi.org/10.1145/3487351.3488363
Diaz, F., Mitra, B., Ekstrand, M. D., Biega, A. J., & Carterette, B. (2020). Evaluating stochastic rankings with expected exposure. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management (pp. 275–284). Association for Computing Machinery. https://doi.org/10.1145/3340531.3411962
Diaz, F. (2024). Pessimistic evaluation. In Proceedings of the 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region (pp. 115–124). Association for Computing Machinery. https://doi.org/10.1145/3673791.3698428
Du, Y., Shi, Y., & Zhao, X. (2007). Using spam farm to boost PageRank. In Proceedings of the 3rd International Workshop on Adversarial Information Retrieval on the Web (pp. 29–36). Association for Computing Machinery. https://doi.org/10.1145/1244408.1244415
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., & Zemel, R. (2012). Fairness through awareness. In Proceedings of the Innovations in Theoretical Computer Science Conference (pp. 214–226). Association for Computing Machinery. https://doi.org/10.1145/2090236.2090255
Ekstrand, M. D., Burke, R., & Diaz, F. (2019). Fairness and discrimination in recommendation and retrieval. In Proceedings of the 13th ACM Conference on Recommender Systems (pp. 576–577). Association for Computing Machinery. https://doi.org/10.1145/3298689.3346964
Ekstrand, M. D., Das, A., Burke, R., & Diaz, F. (2022). Fairness in information access systems. Foundations and Trends in Information Retrieval, 16(1-2), 1-177. https://doi.org/10.48550/arXiv.2105.05779
Ekstrand, M. D., Carterette, B., & Diaz, F. (2024). Distributionally-informed recommender system evaluation. ACM Transactions on Recommender Systems, 2(1), 1-27. https://doi.org/10.1145/3613455
Epps-Darling, A., Bouyer, R. T., & Cramer, H. (2020). Artist gender representation in music streaming. In Proceedings of the 21st International Society for Music Information Retrieval Conference (pp. 248–254). https://archives.ismir.net/ismir2020/paper/000148.pdf (Archived at https://web.archive.org/web/20260210055209/http://archives.ismir.net/ismir2020/paper/000148.pdf)
Eubanks, V. (2018, January). The digital poorhouse. Harper’s Magazine. https://harpers.org/archive/2018/01/the-digital-poorhouse/ (Archived at https://web.archive.org/web/20260210075441/https://harpers.org/archive/2018/01/the-digital-poorhouse/)
Fabris, A., Silvello, G., Susto, G. A., & Biega, A. J. (2023). Pairwise fairness in ranking as a dissatisfaction measure. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining (pp. 931–939). Association for Computing Machinery. https://doi.org/10.1145/3539597.3570459
Feldman, M., Friedler, S. A., Moeller, J., Scheidegger, C., & Venkatasubramanian, S. (2015). Certifying and removing disparate impact. In Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 259–268). Association for Computing Machinery. https://doi.org/10.1145/2783258.2783311
Ferguson, A. G. (2017). The rise of big data policing: Surveillance, race, and the future of law enforcement. New York University Press.
Friedler, S., Scheidegger, C., & Venkatasubramanian, S. (2021). The (im)possibility of fairness: Different value systems require different mechanisms for fair decision making. Communications of the ACM, 64(4), 136–143. https://doi.org/10.1145/3433949
Friedman, B., & Nissenbaum, H. (1996). Bias in computer systems. ACM Transactions on Information Systems, 14(3), 330–347. https://doi.org/10.1145/230538.230561
Fuller, M., & Goffey, A. (2012). Algorithms. In Evil media (pp. 69–82). MIT Press.
Gao, R., & Shah, C. (2021). Addressing bias and fairness in search systems. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 2643–2646). Association for Computing Machinery. https://doi.org/10.1145/3404835.3462807
Gao, R., Ge, Y., & Shah, C. (2022). FAIR: Fairness-aware information retrieval evaluation. Journal of the Association for Information Science and Technology, 73(10), 1461–1473. https://doi.org/10.1002/asi.24648
Gao, R., & Shah, C. (2020). Toward creating a fairer ranking in search engine results. Information Processing & Management, 57(1), 102138. https://doi.org/10.1016/j.ipm.2019.102138
Gebremichael, M. D., & Jackson, J. W. (2006). Bridging the gap in Sub-Saharan Africa: A holistic look at information poverty and the region's digital divide. Government Information Quarterly, 23(2), 267–280. https://doi.org/10.1016/j.giq.2006.02.011
Geyik, S. C., & Kenthapadi, K. (2018, October). Building representative talent search at LinkedIn. LinkedIn Engineering Blog. https://engineering.linkedin.com/blog/2018/10/building-representative-talent-search-at-linkedin (Archived at https://web.archive.org/web/20260123093645/https://www.linkedin.com/blog/engineering/hiring/building-representative-talent-search-at-linkedin)
Gillespie, T. (2014). The relevance of algorithms. In T. Gillespie, P. J. Boczkowski, & K. A. Foot (Eds.), Media technologies: Essays on communication, materiality, and society (pp. 167–193). MIT Press.
Gillespie, T. (2016, February). #trendingistrending: When algorithms become culture. Culture Digitally. http://culturedigitally.org/2016/02/trendingistrending/ (Archived at https://web.archive.org/web/20260218143639/https://culturedigitally.org/2016/02/trendingistrending/)
Gillespie, T. (2019). Algorithmically recognizable: Santorum’s Google problem, and Google’s Santorum problem. In The social power of algorithms (pp. 63–80). Routledge.
Giner, F. (2023). Information retrieval evaluation measures defined on some axiomatic models of preferences. ACM Transactions on Information Systems, 42(3), 1-35. https://doi.org/10.1145/3632171
Gotterbarn, D. W., Brinkman, B., Flick, C., Kirkpatrick, M. S., Miller, K., Vazansky, K., & Wolf, M. J. (2018). ACM code of ethics and professional conduct. https://dora.dmu.ac.uk/server/api/core/bitstreams/1e5b3cb8-2d77-4ab4-885b-d5996b74605f/content (Archived at https://web.archive.org/web/20250501011834/https://dora.dmu.ac.uk/server/api/core/bitstreams/1e5b3cb8-2d77-4ab4-885b-d5996b74605f/content)
Guiver, J., & Snelson, E. (2008). Learning to rank with Softrank and Gaussian processes. In Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 259–266). Association for Computing Machinery. https://doi.org/10.1145/1390334.1390380
Guo, S., Zhang, S., Sun, W., Ren, P., Chen, Z., & Ren, Z. (2023). Towards explainable conversational recommender systems. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 2786–2795). Association for Computing Machinery. https://doi.org/10.1145/3539618.3591884
Hamwela, V., Ahmed, W., & Bath, P. A. (2018). Evaluation of websites that contain information relating to malaria in pregnancy. Public Health, 157, 50–52. https://doi.org/10.1016/j.puhe.2018.01.001
Hargittai, E. (2007). The social, political, economic, and cultural dimensions of search engines: An introduction. Journal of Computer-Mediated Communication, 12(3), 769–777. https://doi.org/10.1111/j.1083-6101.2007.00349.x
Introna, L. D., & Nissenbaum, H. (2000). Shaping the web: Why the politics of search engines matters. The Information Society, 16(3), 169–185. https://doi.org/10.1080/01972240050133634
Jeong, W. (2006). Force feedback textual and graphic displays for the blind. Proceedings of the American Society for Information Science and Technology, 43(1), 1–11. https://doi.org/10.1002/meet.14504301156
Joachims, T. (2021). Fairness and control of exposure in two-sided markets. In Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval (pp. 1–1). Association for Computing Machinery. https://doi.org/10.1145/3471158.3472226
Joachims, T., Granka, L., Pan, B., Hembrooke, H., & Gay, G. (2017). Accurately interpreting clickthrough data as implicit feedback. SIGIR Forum, 51(1), 4–11. https://doi.org/10.1145/3130332.313033
Kay, M., Matuszek, C., & Munson, S. A. (2015). Unequal representation and gender stereotypes in image search results for occupations. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (pp. 3819–3828). Association for Computing Machinery. https://doi.org/10.1145/2702123.270252
Kearns, M., & Roth, A. (2020). The ethical algorithm: The science of socially aware algorithm design. Oxford University Press.
Kırnap, Ö., Diaz, F., Biega, A., Ekstrand, M., Carterette, B., & Yilmaz, E. (2021). Estimation of fair ranking metrics with incomplete judgments. In Proceedings of the Web Conference 2021 (pp. 1065–1075). Association for Computing Machinery. https://doi.org/10.1145/3442381.345008
Kleinberg, J. (1999). Authoritative sources in a hyperlinked environment. Journal of the ACM, 46(5), 604–632. https://doi.org/10.1145/324133.324140
Kontiainen, L., Koulu, R., & Sankari, S. (2022). Research agenda for algorithmic fairness studies: Access to justice lessons for interdisciplinary research. Frontiers in Artificial Intelligence, 5, 882134. https://doi.org/10.3389/frai.2022.882134
Koskela, M., Luukkonen, P., Ruotsalo, T., Sjöberg, M., & Floréen, P. (2018). Proactive information retrieval by capturing search intent from primary task context. ACM Transactions on Interactive Intelligent Systems, 8(3), 1–25. https://doi.org/10.1145/3150975
Krämer, T., Papenmeier, A., Carevic, Z., Kern, D., & Mathiak, B. (2021). Data-seeking behaviour in the social sciences. International Journal on Digital Libraries, 22, 175–195. https://doi.org/10.1007/s00799-021-00303-0
Krestel, R., Fankhauser, P., & Nejdl, W. (2009). Latent Dirichlet allocation for tag recommendation. In Proceedings of the Third ACM Conference on Recommender Systems (pp. 61–68). Association for Computing Machinery. https://doi.org/10.1145/1639714.1639726
Kroll, J. A., Huey, J., Barocas, S., Felten, E. W., Reidenberg, J. R., Robinson, D. G., & Yu, H. (2017). Accountable algorithms. University of Pennsylvania Law Review, 165, 633–705. https://repository.law.upenn.edu/Documents/Detail/accountable-algorithms/155735 (Archived at https://web.archive.org/web/20260218210455/https://repository.law.upenn.edu/Documents/Detail/accountable-algorithms/155735)
Kumar, R., Raghavan, P., Rajagopalan, S., & Tomkins, A. (2006). Core algorithms in the CLEVER system. ACM Transactions on Internet Technology, 6(2), 131–152. https://doi.org/10.1145/1149121.1149123
Lamprier, S., Amghar, T., Saubion, F., & Levrat, B. (2010). Traveling among clusters: A way to reconsider the benefits of the cluster hypothesis. In Proceedings of the 2010 ACM Symposium on Applied Computing (pp. 1774–1780). Association for Computing Machinery. https://doi.org/10.1145/1774088.1774465
Latzer, M., Hollnbuchner, K., Just, N., & Saurwein, F. (2014). The economics of algorithmic selection on the internet (Working Paper – Media Change & Innovation Division). University of Zurich. https://doi.org/10.4337/9780857939852.00028
Lederman, N. G., & Lederman, J. S. (2015). What is a theoretical framework? A practical answer. Journal of Science Teacher Education, 26(7), 593–597. https://doi.org/10.1007/s10972-015-9443-2
Lewandowski, D., & Spree, U. (2011). Ranking of Wikipedia articles in search engines revisited: Fair ranking for reasonable quality? Journal of the American Society for Information Science and Technology, 62(1), 117–132. https://doi.org/10.1002/asi.21423
Li, N., Anderson, A. A., Brossard, D., & Scheufele, D. A. (2014). Channeling science information seekers’ attention? A content analysis of top-ranked vs. lower-ranked sites in Google. Journal of Computer-Mediated Communication, 19(3), 562–575. https://doi.org/10.1111/jcc4.12043
MacCarthy, M. (2019). Fairness in algorithmic decision-making. Brookings Institution. https://www.brookings.edu/articles/fairness-in-algorithmic-decision-making/ (Archived at https://web.archive.org/web/20260322175848/https://www.brookings.edu/articles/fairness-in-algorithmic-decision-making/)
Mackenzie, A. (2015). The production of prediction: What does machine learning want? European Journal of Cultural Studies, 18(4–5), 429–445. https://doi.org/10.1177/1367549415577384
Makri, S., McKay, D., Buchanan, G., Chang, S., Lewandowski, D., MacFarlane, A., Cole, L., Vrijenhoek, S., & Ferraro, A. (2021). Search a great leveler? Ensuring more equitable information acquisition. Proceedings of the Association for Information Science and Technology, 58(1), 613–618. https://doi.org/10.1002/pra2.511
Mansoury, M., Abdollahpouri, H., Pechenizkiy, M., Mobasher, B., & Burke, R. (2020). Feedback loop and bias amplification in recommender systems. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management (pp. 2145–2148). Association for Computing Machinery. https://doi.org/10.1145/3340531.3412152
McKearney, R. M., MacKinnon, R. C., Smith, M., & Baker, R. (2018). Tinnitus information online—does it ring true? The Journal of Laryngology & Otology, 132(11), 984–989. https://doi.org/10.1017/S0022215118001792
Menczer, F., Pant, G., & Srinivasan, P. (2004). Topical web crawlers: Evaluating adaptive algorithms. ACM Transactions on Internet Technology, 4(4), 378–419. https://doi.org/10.1145/1031114.1031117
Moses, L. B., & Chan, J. (2018). Algorithmic prediction in policing: Assumptions, evaluation, and accountability. Policing and Society, 28(7), 806–822. https://doi.org/10.1080/10439463.2016.1253695
Moulin, H. (2004). Fair division and collective welfare. MIT press.
Mukherjee, S., & Weikum, G. (2015). Leveraging joint interactions for credibility analysis in news communities. In Proceedings of the 24th ACM International Conference on Information and Knowledge Management (pp. 353–362). Association for Computing Machinery. https://doi.org/10.1145/2806416.2806537
Nahon, K. (2016). Where there is social media, there is politics. In A. Bruns, E. Skogerbø, C. Christensen, A. O. Larsson, & G. S. Enli (Eds.), The Routledge companion to social media and politics (pp. 39–55). Routledge. https://doi.org/10.4324/9781315716299
Nissenbaum, H. (2001). How computer systems embody values. Computer, 34(3), 118–119. https://doi.org/10.1109/2.910905
Noble, S. (2012). Missed Connections: What Search Engines Say about Women. Bitch magazine, 12(4): 37-41. https://www.academia.edu/1975319/Missed_Connections_What_Search_Engines_Say_About_Women
Noble, S. (2018). Algorithms of oppression: Race, gender, and power in the digital age. NYU Press.
O’Neil, C. (2017). Weapons of math destruction: How big data increases inequality and threatens democracy. Penguin Books.
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., McGuinness, L. A., & Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
Pariser, E. (2012). The filter bubble: How the new personalized web is changing what we read and how we think. Penguin Books.
Park, Y. J., & Jang, S. M. (2016). African American Internet use for information search and privacy protection tasks. Social Science Computer Review, 34(5), 618–630. https://doi.org/10.1177/08944393155974
Pasquale, F. (2015). The black box society: The secret algorithms that control money and information. Harvard University Press.
Peter, F. (2017). Political legitimacy. In E. N. Zalta (Ed.), The Stanford encyclopedia of philosophy. https://plato.stanford.edu/archives/sum2017/entries/legitimacy/
Rahimi, M. A., & Eulenberg, J. B. (1974). A computing environment for the blind. In Proceedings of the National Computer Conference and Exposition (pp. 121–124). Association for Computing Machinery. https://doi.org/10.1145/1500175.1500197
Rekabsaz, N., Kopeinik, S., & Schedl, M. (2021). Societal biases in retrieved contents: Measurement framework and adversarial mitigation of BERT rankers. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 306–316). Association for Computing Machinery. https://doi.org/10.1145/3404835.3462949
Rey, B., & Kannan, A. (2010). Conversion rate based bid adjustment for sponsored search. In Proceedings of the 19th International Conference on World Wide Web (pp. 1173–1174). Association for Computing Machinery. https://doi.org/10.1145/1772690.1772860
Richardson, R., Schultz, J. M., & Crawford, K. (2019). Dirty data, bad predictions: How civil rights violations impact police data, predictive policing systems, and justice. New York University Law Review, 94(1), 15–55. https://nyulawreview.org/online-features/dirty-data-bad-predictions-how-civil-rights-violations-impact-police-data-predictive-policing-systems-and-justice/ (Archived at https://web.archive.org/web/20260416223004/https://nyulawreview.org/online-features/dirty-data-bad-predictions-how-civil-rights-violations-impact-police-data-predictive-policing-systems-and-justice/)
Rieger, A., Draws, T., Theune, M., & Tintarev, N. (2024). Nudges to mitigate confirmation bias during web search on debated topics: Support vs. manipulation. ACM Transactions on the Web, 18(2), 1–27. https://doi.org/10.1145/3635034
Rolf, E., Worledge, T., Recht, B., & Jordan, M. I. (2021). Representation matters: Assessing the importance of subgroup allocations in training data. arXiv preprint arXiv:2103.03399. https://doi.org/10.48550/arXiv.2103.03399
Roy, D., Mitra, M., & Ganguly, D. (2018). To clean or not to clean: Document preprocessing and reproducibility. Journal of Data and Information Quality, 10(4), Article 18, 1–25. https://doi.org/10.1145/3242180
Sakai, T., Kim, J. Y., & Kang, I. (2023). A versatile framework for evaluating ranked lists in terms of group fairness and relevance. ACM Transactions on Information Systems, 42(1), 1–36. https://doi.org/10.1145/3589763
Sambasivan, N., Arnesen, E., Hutchinson, B., & Prabhakaran, V. (2020). Non-portability of algorithmic fairness in India. arXiv preprint arXiv:2012.03659. https://doi.org/10.48550/arXiv.2012.03659
Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J. (2019). Fairness and abstraction in sociotechnical systems. In Proceedings of the Conference on Fairness, Accountability, and Transparency (pp. 59–68). Association for Computing Machinery. https://doi.org/10.1145/3287560.3287598
Schmidt, C., Gorman, T. J., Bayor, A. A., & Gary, M. S. (2010). Impact of low-cost, on-demand information access in a remote Ghanaian village. In Proceedings of the 4th ACM/IEEE International Conference on Information and Communication Technologies and Development (pp. 1–10). Association for Computing Machinery. https://dl.acm.org/doi/10.1145/2369220.2369261 Shah, C., & Bender, E. M. (2024). Envisioning information access systems: What makes for good tools and a healthy Web? ACM Transactions on the Web, 18(3), 1–24. https://doi.org/10.1145/3649468
Sonboli, N., Burke, R., Ekstrand, M., & Mehrotra, R. (2022). The multisided complexity of fairness in recommender systems. AI Magazine, 43(2), 164–176. https://doi.org/10.1002/aaai.12054
Sonnenberg, C. (2020). E-government and social media: The impact on accessibility. Journal of Disability Policy Studies, 31(3), 181-191. https://doi.org/10.1177/1044207320906521
Spinelli, L., & Crovella, M. (2020). How YouTube leads privacy-seeking users away from reliable information. In Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization (pp. 244–251). Association for Computing Machinery. https://doi.org/10.1145/3386392.3399566
Sun, Z., Di, L., Heo, G., Zhang, C., Fang, H., Yue, P., Wang, X., & Lin, L. (2017). GeoFairy: Towards a one-stop and location-based service for geospatial information retrieval. Computers, Environment and Urban Systems, 62, 156–167. https://doi.org/10.1016/j.compenvurbsys.2016.11.007
Sun, W., Nasraoui, O., & Shafto, P. (2018). Iterated algorithmic bias in the interactive machine learning process of information filtering. In Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (pp. 108–116). SCITEPRESS. https://doi.org/10.5220/0006938301100118
Tufekci, Z. (2015). Algorithmic harms beyond Facebook and Google: Emergent challenges of computational agency. Journal on Telecommunications & High Technology Law, 13(2), 203–216. https://ctlj.colorado.edu/wp-content/uploads/2015/08/Tufekci-final.pdf (Archived at https://web.archive.org/web/20260421173754/https://ctlj.colorado.edu/wp-content/uploads/2015/08/Tufekci-final.pdf)
Udoh, E. S., Yuan, X., & Rorissa, A. (2022). Rethinking algorithmic fairness in the context of information access. Proceedings of the Association for Information Science and Technology, 59(1), 815–817. https://doi.org/10.1002/pra2.736
Udoh, E., Yuan, X., & Rorissa, A. (2024). A framework for defining algorithmic fairness in the context of information access. Proceedings of the Association for Information Science and Technology, 61(1), 667–672. https://doi.org/10.1002/pra2.1077
Venkatasubramanian, S., Scheidegger, C., Friedler, S., & Clauset, A. (2021). Fairness in networks: Social capital, information access, and interventions. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 4078–4079). Association for Computing Machinery. https://doi.org/10.1145/3447548.3470821
Wachter, S., & Mittelstadt, B. (2019). A right to reasonable inferences: Re-thinking data protection law in the age of big data and AI. Columbia Business Law Review, 2019(2), 494–620. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3248829 (Archived at https://web.archive.org/web/20260414121218/https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3248829)
Webb, H., Koene, A., Patel, M., & Vallejos, E. P. (2018). Multi-stakeholder dialogue for policy recommendations on algorithmic fairness. In Proceedings of the 9th International Conference on Social Media and Society (pp. 395–399). Association for Computing Machinery. https://doi.org/10.1145/3217804.3217952
White, R. W., & Hassan, A. (2014). Content bias in online health search. ACM Transactions on the Web, 8(4), 1–33. https://doi.org/10.1145/2663355
Wu, H., Mitra, B., Ma, C., Diaz, F., & Liu, X. (2022). Joint multisided exposure fairness for recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 703–714). Association for Computing Machinery. https://doi.org/10.1145/3477495.3532007
Yang, Y. T., & Chen, B. (2015). Web accessibility for older adults: A comparative analysis of disability laws. The Gerontologist, 55(5), 854–865. https://doi.org/10.1093/geront/gnv057
Yue, Y., Patel, R., & Roehrig, H. (2010). Beyond position bias: Examining result attractiveness as a source of presentation bias in clickthrough data. In Proceedings of the 19th International Conference on the World Wide Web (pp. 1011–1018). Association for Computing Machinery. https://doi.org/10.1145/1772690.1772793
Zambonelli, F., Salim, F., Loke, S. W., De Meuter, W., & Kanhere, S. (2018). Algorithmic governance in smart cities: The conundrum and the potential of pervasive computing solutions. IEEE Technology and Society Magazine, 37(2), 80–87. https://doi.org/10.1109/MTS.2018.2826080
Zarsky, T. (2016). The trouble with algorithmic decisions: An analytic road map to examine efficiency and fairness in automated and opaque decision making. Science, Technology, & Human Values, 41(1), 118–132. https://www.jstor.org/stable/43671285
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Emmanuel Sebastian Udoh, Xiaojun Yuan, Abebe Rorrissa
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
