Bias on the web
Abstract
Bias in Web data and use taints the algorithms behind Web-based applications, delivering equally biased results.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Bias is not only algorithmic, but extends beyond the limits of models in a vicious cycle: human activity bias, data bias, leads to sampling bias on the web, algorithmic bias, interaction bias and finally, self-selection bias that can revert back into the algorithm a second-order bias.
“As has been shown in the previous dimension, bias is not only algorithmic, but extends beyond the limits of models in a vicious cycle: starting with human activity bias, data bias, leads to sampling bias on the web (specially to be considered in the use of data to learn generative models), algorithmic bias, interaction bias and finally, self-selection bias that can revert back into the algorithm a”
From Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation (2023) · cites this paper as [84]
Interaction biases between employers and hiring technology can amplify tiny differences in algorithmic output.
“Interaction biases between employers and hiring technology can amplify tiny differences in algorithmic output [16].”
From Fairness and Bias in Algorithmic Hiring: A Multidisciplinary Survey (2025) · cites this paper as [16]
Cites
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
Bolukbasi, T., Chang, K.-W., Zou, J., Saligrama, V., & Kalai, A. (2016) · arXiv · doi:10.48550/arxiv.1607.06520 · Cited by 1,615 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
A Survey on Bias and Fairness in Machine Learning
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2022) · ACM Computing Surveys · doi:10.1145/3457607 · Cited by 5,812 · see citing works
“sented [8]. For example, on the Web everything seen by the user can get clicks, while everything else gets no click. And it could be the case that the user does not see all the information on the Web [8]. Ranking Bias. The top-ranked result will attract more clicks than the others because it is both the most relevant…”
BackgroundInfluentialHogan, A., Blomqvist, E., Cochez, M., d’Amato, C., de Melo, G., Gutiérrez, C., Kirrane, S., Gayo, J. E. L., Roberto Navigli, A., Neumaier, S., et al. (2021) · ACM Computing Surveys · doi:10.1145/3447772 · Cited by 2,371 · see citing works
Bias in data‐driven artificial intelligence systems—An introductory survey
Ntoutsi, E., Fafalios, P., Gadiraju, U., Iosifidis, V., Nejdl, W., Vidal, M., Ruggieri, S., Turini, F., Papadopoulos, S., Krasanakis, E., et al. (2020) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1356 · Cited by 1,043 · see citing works
“By Web Scientists.”
BackgroundInfluentialShneiderman, B. (2020) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3419764 · Cited by 768 · see citing works
“Baeza-Yates [5] described additional forms of bias, such as geography, language, and culture, which were embedded in web-based algorithms, databases, and user interfaces.”
BackgroundDíaz-Rodríguez, N., Del Ser, J., Coeckelbergh, M., López de Prado, M., Herrera-Viedma, E., & Herrera, F. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101896 · Cited by 800 · see citing works
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kıcıman, E. (2019) · Frontiers in Big Data · doi:10.3389/fdata.2019.00013 · Cited by 763 · see citing works
Rodríguez, N. D., del Ser, J., Coeckelbergh, M., de Prado, M. L., Herrera-Viedma, E., & Herrera, F. (2023) · Information Fusion · doi:10.48550/arxiv.2305.02231 · Cited by 674 · see citing works
“As has been shown in the previous dimension, bias is not only algorithmic, but extends beyond the limits of models in a vicious cycle: starting with human activity bias, data bias, leads to sampling bias on the web (specially to be considered in the use of data to learn generative models), algorithmic bias, interactio…”
BackgroundTowards a standard for identifying and managing bias in artificial intelligence
Schwartz, R., Vassilev, A., Greene, K., Perine, L., Burt, A., & Hall, P. (2022) · doi:10.6028/nist.sp.1270 · Cited by 548 · see citing works
Generative artificial intelligence
Banh, L., & Strobel, G. (2023) · Electronic Markets · doi:10.1007/s12525-023-00680-1 · Cited by 722 · see citing works
“Furthermore, social bias as a form of data bias can cause distorted views in generated texts or images and should be considered as well as mitigated (Baeza-Yates, 2018).”
BackgroundBelenguer, L. (2022) · AI and Ethics · doi:10.1007/s43681-022-00138-8 · Cited by 289 · see citing works
A clarification of the nuances in the fairness metrics landscape
Castelnovo, A., Crupi, R., Greco, G., Regoli, D., Penco, I., & Cosentini, A. (2021) · Scientific Reports · doi:10.1038/s41598-022-07939-1 · Cited by 283 · see citing works
Ferrara, E. (2024) · Journal of Computational Social Science · doi:10.1007/s42001-024-00250-1 · Cited by 296 · see citing works
“From the subtle perpetuation of biases to the blatant reinforcement of stereotypes [2, 6, 21], GenAI can become mirrors reflecting and amplifying the imperfections of our society [1, 8].”
Controlling Fairness and Bias in Dynamic Learning-to-Rank
Morik, M., Singh, A., Hong, J., & Joachims, T. (2020) · Annual International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3397271.3401100 · Cited by 260 · see citing works
van Giffen, B., Herhausen, D., & Fahse, T. (2022) · Journal of Business Research · doi:10.1016/j.jbusres.2022.01.076 · Cited by 230 · see citing works
“However, to date only a few scattered articles provide a more detailed examination of certain ML biases and mitigation methods (e.g., Baeza-Yates, 2018; Mehrabi et al., 2019; Silva & Kenney, 2019; Suresh & Guttag, 2019), and across these articles the respective terminologies differ substantially.”
BackgroundFairness in rankings and recommendations: an overview
Pitoura, E., Stefanidis, K., & Koutrika, G. (2021) · The VLDB Journal · doi:10.1007/s00778-021-00697-y · Cited by 150 · see citing works
Fairness in recommender systems: research landscape and future directions
Deldjoo, Y., Jannach, D., Bellogín, A., Difonzo, A., & Zanzonelli, D. (2023) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-023-09364-z · Cited by 258 · see citing works
“As discussed above, one frequently discussed topic in the area of recommender systems is the problem of biased data (Chen et al. 2022; Baeza-Yates 2018).”
Proppy: Organizing the news based on their propagandistic content
Barrón-Cedeño, A., Jaradat, I., Martino, G. D. S., & Nakov, P. (2019) · Information Processing & Management · doi:10.1016/j.ipm.2019.03.005 · Cited by 204 · see citing works
“Bias must be considered when addressing people’s information needs, as it affects us all and much of the time we are unaware of it [7].”
BackgroundAI Fairness in Data Management and Analytics: A Review on Challenges, Methodologies and Applications
Chen, P., Wu, L., & Wang, L. (2023) · Applied Sciences · doi:10.3390/app131810258 · Cited by 155 · see citing works
Investigating gender fairness of recommendation algorithms in the music domain
Melchiorre, A. B., Rekabsaz, N., Parada‐Cabaleiro, E., Brandl, S., Lesota, O., & Schedl, M. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102666 · Cited by 153 · see citing works
“These imbalances can be classified as population bias (Olteanu, Castillo, Diaz, & Kiciman, 2019), which is part of a more general data bias (Baeza-Yates, 2018).”
Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond
Nasir, S., Khan, R. A., & Bai, S. (2024) · IEEE Access · doi:10.1109/access.2024.3369912 · Cited by 152 · see citing works
Drukker, K., Chen, W., Gichoya, J. W., Gruszauskas, N. P., Kalpathy-Cramer, J., Koyejo, S., Myers, K. J., Sá, R. C., Sahiner, B., Whitney, H. M., et al. (2023) · Journal of Medical Imaging · doi:10.1117/1.jmi.10.6.061104 · Cited by 149 · see citing works
Bogina, V., Hartman, A., Kuflik, T., & Shulner-Tal, A. (2022) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-021-00248-0 · Cited by 142 · see citing works
Himeur, Y., Sohail, S. S., Bensaali, F., Amira, A., & Alazab, M. (2022) · Computers & Security · doi:10.1016/j.cose.2022.102746 · Cited by 139 · see citing works
Understanding and Mitigating Worker Biases in the Crowdsourced Collection of Subjective Judgments
Hube, C., Fetahu, B., & Gadiraju, U. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300637 · Cited by 129 · see citing works
“Recent work has reflected on the importance of creating an awareness of existing biases in order to alleviate the biases [2].”
BackgroundFairness in Recommendation: Foundations, Methods, and Applications
Li, Y., Chen, H., Xu, S., Ge, Y., Tan, J., Liu, S., & Zhang, Y. (2023) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3610302 · Cited by 147 · see citing works
“Moreover, unfairness can arise from the biases in model designing, model training, and model evaluating processes, such as the biased model architecture, improper use of certain optimization methods or estimators, and inappropriate benchmarks [16].”
BackgroundFairness in Ranking, Part I: Score-Based Ranking
Zehlike, M., Yang, K., & Stoyanovich, J. (2022) · ACM Computing Surveys · doi:10.1145/3533379 · Cited by 129 · see citing works
“Position bias arises because in Western cultures we read from top to bottom, and from left to right, and so items appearing in the top-left corner of the screen attract more attention [7].”
BackgroundHuman Reliance on Machine Learning Models When Performance Feedback is Limited: Heuristics and Risks
Lu, Z., & Yin, M. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445562 · Cited by 131 · see citing works
Evolution and impact of bias in human and machine learning algorithm interaction
Sun, W., Nasraoui, O., & Shafto, P. (2020) · PLOS ONE · doi:10.1371/journal.pone.0235502 · Cited by 116 · see citing works
Fairness and Bias in Algorithmic Hiring: A Multidisciplinary Survey
Fabris, A., Baranowska, N., Dennis, M. J., Graus, D., Hacker, P., Saldivar, J., Zuiderveen Borgesius, F., & Biega, A. J. (2025) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3696457 · Cited by 117 · see citing works
Towards risk-aware artificial intelligence and machine learning systems: An overview
Zhang, X., Chan, F. T. S., Yan, C., & Bose, I. (2022) · Decision Support Systems · doi:10.1016/j.dss.2022.113800 · Cited by 113 · see citing works
Chang, Y.-L., & Ke, J. (2023) · Human Resource Development Review · doi:10.1177/15344843231200930 · Cited by 105 · see citing works
“Second, algorithmic bias is systematic biases generated by the algorithm that can magnify, operationalize, and institutionalize biases, and persists even after introducing new data to retrain the model (Akter et al., 2021; Baeza-Yates, 2018; Getoor, 2019).”
MethodKnowing About Knowing: An Illusion of Human Competence Can Hinder Appropriate Reliance on AI Systems
He, G., Kuiper, L., & Gadiraju, U. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581025 · Cited by 107 · see citing works
Filter bubbles in recommender systems: Fact or fallacy—A systematic review
Areeb, Q., Nadeem, M., Sohail, S., Imam, R., Doctor, F., Himeur, Y., Hussain, A., & Amira, A. (2023) · WIREs Data. Mining. Knowl. Discov. · doi:10.1002/widm.1512 · Cited by 101 · see citing works
“The design of the user interaction is also critical, as it can introduce additional biases in the form of presentation or exposure bias (Baeza-Yates, 2018).”
BackgroundInfluentialFerrara, E. (2019) · Communications of the ACM · doi:10.1145/3299768 · Cited by 101 · see citing works
“major problems of modern-days AIs (and, in general, of supervised learning approaches based on Big Data) is that biases learned from training data will propagate into predictions. The problem of bias [5], especially in AI, is under the spotlight and is being tackled by the computing research community.7 One way an AI…”
BackgroundSocial Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kiciman, E. (2016) · doi:10.2139/ssrn.2886526 · Cited by 763 · see citing works
Fairness in Ranking, Part II: Learning-to-Rank and Recommender Systems
Zehlike, M., Yang, K., & Stoyanovich, J. (2022) · ACM Computing Surveys · doi:10.1145/3533380 · Cited by 93 · see citing works
“…] is the probability mass function over the ranking space, and position bias v ( τ ( i )) refers to the observation that users of a ranking system tend to prefer candidates at higher positions, and that their attention decreases either geometrically or logarithmically with increasing rank [3, 23].”
BackgroundNeutral bots probe political bias on social media
Chen, W., Pacheco, D. F., Yang, K.-C., & Menczer, F. (2021) · Nature Communications · doi:10.1038/s41467-021-25738-6 · Cited by 66 · see citing works
Trustworthy Distributed AI Systems: Robustness, Privacy, and Governance
Wei, W., & Liu, L. (2024) · ACM Computing Surveys · doi:10.1145/3645102 · Cited by 88 · see citing works
“In addition to biases in data, the prediction models trained by learning algorithms can be biased due to certain algorithm design choices, such as optimization strategies, regularization functions, or biased estimators [208].”
Social media data for disaster risk management and research
Erokhin, D., & Komendantova, N. (2024) · International Journal of Disaster Risk Reduction · doi:10.1016/j.ijdrr.2024.104980 · Cited by 88 · see citing works
Rekabsaz, N., Kopeinik, S., & Schedl, M. (2021) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3404835.3462949 · Cited by 73 · see citing works
Fairness issues, current approaches, and challenges in machine learning models
Jui, T. D., & Rivas, P. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-023-02083-2 · Cited by 91 · see citing works
Fair ranking: a critical review, challenges, and future directions
Patro, G. K., Porcaro, L., Mitchell, L., Zhang, Q., Zehlike, M., & Garg, N. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3533238 · Cited by 61 · see citing works
“However, like all machine learning techniques, such systems have been found to ‘unfairly’ favor or discriminate against certain individuals or groups of individuals in various scenarios [47, 9, 35].”
BackgroundBias in algorithms of AI systems developed for COVID-19: A scoping review
Delgado, J., de Manuel, A., Parra, I., Moyano, C., Rueda, J., Guersenzvaig, A., Ausin, T., Cruz, M., Casacuberta, D., & Puyol, A. (2022) · Journal of Bioethical Inquiry · doi:10.1007/s11673-022-10200-z · Cited by 70 · see citing works
“Many papers have already stressed the matter of bias in artificial intelligence in different areas, such as the content and use of websites (Baeza-Yates, 2018), the process of recruitment (Dastin 2018), the racial bias in assessment in criminal sentences (Angwin et al. 2016), or the racial bias in…”
BackgroundData Integration by Combining Big Data and Survey Sample Data for Finite Population Inference
Kim, J.-K., & Tam, S.-M. (2020) · International Statistical Review · doi:10.1111/insr.12434 · Cited by 67 · see citing works
“(1992), Chambers and Clark (2012). However, if the data set comes from a non-probability sample, e.g. from a big data source, the estimation is less straightforward, and the theory for making inference with non-probability samples is not fully developed. Tam and Clarke (2015) and Pfefffermann (2015) addressed methodol…”
BackgroundInfluentialCognitive Biases in Fact-Checking and Their Countermeasures: A Review
Soprano, M., Roitero, K., Barbera, D. L., Ceolin, D., Spina, D., Demartini, G., & Mizzaro, S. (2024) · Information Processing & Management · doi:10.1016/j.ipm.2024.103672 · Cited by 68 · see citing works
Gender biases within Artificial Intelligence and ChatGPT: Evidence, Sources of Biases and Solutions
Ho, J. Q. H., Hartanto, A., Koh, A., & Majeed, N. M. (2025) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2025.100145 · Cited by 74 · see citing works
Analyzing the impact of missing values and selection bias on fairness
Wang, Y., & Singh, L. (2021) · International Journal of Data Science and Analytics · doi:10.1007/s41060-021-00259-z · Cited by 49 · see citing works
Establishing and evaluating trustworthy AI: overview and research challenges
Kowald, D., Scher, S., Pammer-Schindler, V., Müllner, P., Waxnegger, K., Demelius, L., Fessl, A., Toller, M., Mendoza Estrada, I. G., Šimić, I., et al. (2024) · Frontiers in Big Data · doi:10.3389/fdata.2024.1467222 · Cited by 59 · see citing works
“This problem becomes even more challenging in evolving AI systems because they can potentially reinforce bias between the user population, data, and algorithm (Baeza-Yates, 2018).”
Bias in Search and Recommender Systems
Baeza-Yates, R. (2020) · ACM Conference on Recommender Systems · doi:10.1145/3383313.3418435 · Cited by 59 · see citing works
Self-citationWho Counts as a Notable Sociologist on Wikipedia? Gender, Race, and the “Professor Test”
Adams, J., Brückner, H., & Naslund, C. (2019) · Socius Sociological Research for a Dynamic World · doi:10.1177/2378023118823946 · Cited by 58 · see citing works
“Yet they also differ insofar as they include characteristics of the digital sphere, such as algorithmic bias (Baeza-Yates 2018) or emergent forms of action deriving from online anonymity.”
BackgroundAligning Eyes between Humans and Deep Neural Network through Interactive Attention Alignment
Gao, Y., Sun, T., Zhao, L., & Hong, S.-S. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3555590 · Cited by 38 · see citing works
“…of “bias” can be broadly defined [Chouldechova and Roth 2018; Saxena et al. 2019], studies generally agree that one of the major sources that cause bias is associated with the bias already encoded in data used for training models [Baeza-Yates 2018; Burns et al. 2018; Chouldechova and Roth 2018].”
MethodDash, A., Shandilya, A., Biswas, A., Ghosh, K., Ghosh, S., & Chakraborty, A. (2018) · Proc. ACM Hum. Comput. Interact. · doi:10.1145/3359274 · Cited by 57 · see citing works
“In [6], Baeza-Yates has discussed how human perceptions and societal biases creep into social media, and how different algorithms fortify them.”
BackgroundArtificial intelligence governance: Understanding how public organizations implement it
de Almeida, P. G. R., & dos Santos, C. D. (2025) · Government Information Quarterly · doi:10.1016/j.giq.2024.102003 · Cited by 78 · see citing works
Draws, T., Tintarev, N., Gadiraju, U., Bozzon, A., & Timmermans, B. (2021) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3404835.3462851 · Cited by 38 · see citing works
“Given that real search result rankings are often biased concerning viewpoints [18, 51, 58–60], SEME is a pressing concern [7, 9].”
BackgroundAhmmad, M., Shahzad, K., Iqbal, A., & Latif, M. (2025) · Societies · doi:10.3390/soc15110301 · Cited by 72 · see citing works
Partisan search behavior and Google results in the 2018 U.S. midterm elections
Trielli, D., & Diakopoulos, N. A. (2020) · Information Communication & Society · doi:10.1080/1369118x.2020.1764605 · Cited by 61 · see citing works
“Searching is in itself imbued with the bias of searchers (Baeza-Yates, 2018).”
BackgroundSánchez-Junquera, J., Chulvi, B., Rosso, P., & Ponzetto, S. P. (2021) · Applied Sciences · doi:10.3390/app11083610 · Cited by 34 · see citing works
Situated algorithms: a sociotechnical systemic approach to bias
Draude, C., Klumbytė, G., Lücking, P., & Treusch, P. (2019) · Online Information Review · doi:10.1108/oir-10-2018-0332 · Cited by 64 · see citing works
Same-day delivery with fair customer service
Chen, X., Wang, T., Thomas, B. W., & Ulmer, M. (2020) · European Journal of Operational Research · doi:10.1016/j.ejor.2022.12.009 · Cited by 49 · see citing works
“Algorithmic bias refers to the occurrence of unfair outcomes due to a direct consequence of the objective (Baeza-Yates 2018).”
BackgroundChen, K., Duan, Z., & Yang, S. (2021) · Politics and the Life Sciences · doi:10.1017/pls.2021.19 · Cited by 51 · see citing works
“Researchers also need to be aware that the data generation from social media can suffer problems such as algorithmic bias, polarization, and segregation (Baeza-Yates, 2018; Colleoni et al., 2014; Lawrence et al., 2010).”
BackgroundFairness metrics and bias mitigation strategies for rating predictions
Ashokan, A., & Haas, C. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102646 · Cited by 47 · see citing works
Do neural ranking models intensify gender bias?
Rekabsaz, N., & Schedl, M. (2020) · Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3397271.3401280 · Cited by 64 · see citing works
“The existence of various types of bias in IR, and the concerns regarding its implications on IR applications, have been pointed out in several previous studies [1, 2, 5, 11].”
ResultEmpirical Analysis of Bias in Voice-based Personal Assistants
Lima, L., Furtado, V., Furtado, E., & Almeida, V. A. F. (2019) · The Web Conference · doi:10.1145/3308560.3317597 · Cited by 46 · see citing works
“It cannot be strictly classified as a bias of interaction cf. [1 ], because the latter typically concentrate on the user interface and the user’s own self-selected, biased interaction.”
BackgroundAlgorithmic bias and the Value Sensitive Design approach
Simon, J., Wong, P.-H., & Rieder, G. (2020) · Internet Policy Review · doi:10.14763/2020.4.1534 · Cited by 43 · see citing works
“, Baeza-Yates, 2018; Mehrabi et al., 2019; Olteanu et al., 2019). Barocas and Selbst (2016), for instance, provide a detailed description of the different ways that biases can be introduced into a machine learning system, including (i) through problem specification, where the definition of target variables rests on su…”
“I Can’t Reply with That”: Characterizing Problematic Email Reply Suggestions
Robertson, R. E., Olteanu, A., Diaz, F., Shokouhi, M., & Bailey, P. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445557 · Cited by 43 · see citing works
Constantinides, M., Bogucka, E. P., Quercia, D., Kallio, S., & Tahaei, M. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3686927 · Cited by 34 · see citing works
“While AI holds the potential to benefit humanity, concerns regarding biases [7, 15, 20] and the lack of transparency and accountability [66, 78] hinder its ability to unlock human capabilities on a large scale.”
A review on individual and multistakeholder fairness in tourism recommender systems
Banerjee, A., Banik, P., & Wörndl, W. (2023) · Frontiers in Big Data · doi:10.3389/fdata.2023.1168692 · Cited by 40 · see citing works
“Apart from studies that have examined the impact of search rankings and position bias in different information retrieval scenarios [such as (Fortunato et al., 2006; Craswell et al., 2008; Chuklin et al., 2015; Baeza-Yates, 2018; Ursu, 2018; Geyik et al., 2019; Draws et al., 2021)], there have also been studies that”
Dataset Reuse: Toward Translating Principles to Practice
Koesten, L. M., Vougiouklis, P., Simperl, E., & Groth, P. (2020) · Patterns · doi:10.1016/j.patter.2020.100136 · Cited by 40 · see citing works
“69 This indicates that data quality is inseparable from documentation efforts, be that as metadata or other forms of contextual material.”
Ferrara, E. (2023) · doi:10.2139/ssrn.4614223 · Cited by 266 · see citing works
Detecting race and gender bias in visual representation of AI on web search engines
Makhortykh, M., Urman, A., & Ulloa, R. (2021) · International Workshop on Algorithmic Bias in Search and Recommendation · doi:10.1007/978-3-030-78818-6_5 · Cited by 37 · see citing works
Managing Bias in Machine Learning Projects
Fahse, T., Huber, V., & van Giffen, B. (2021) · Lecture notes in information systems and organisation · doi:10.1007/978-3-030-86797-3_7 · Cited by 54 · see citing works
The few-get-richer: a surprising consequence of popularity-based rankings?
Germano, F., Gómez, V., & Mens, G. L. (2019) · The Web Conference · doi:10.1145/3308558.3313693 · Cited by 37 · see citing works
“ng algorithms may impact society, playing an active role in the spread of misinformation [29], political polarization [11], or trustworthiness [14]. They might also reinforce existing judgment biases [2]. Rankings systematically affect the information people access about products, services, events, or ideas, because u…”
BackgroundAssessing Viewpoint Diversity in Search Results Using Ranking Fairness Metrics
Draws, T., Tintarev, N., Gadiraju, U., Bozzon, A., & Timmermans, B. (2020) · SIGKDD Explorations · doi:10.1145/3468507.3468515 · Cited by 36 · see citing works
“e learning systems extends into several dierent sub-elds [4,13]. One of these sub-elds { fair ranking { has received increasing attention recently, following calls for dealing with bias on the web [3]. This has led to the development of evaluative frameworks [5,16] and metrics [11,20] for assessing bias and fairness i…”
BackgroundNeumann, T., De‐Arteaga, M., & Fazelpour, S. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3533205 · Cited by 23 · see citing works
“Concerns over disparities in evidence retrieval are primarily grounded in properties of the current fact-checking ecosystem [48] and dynamics of the way information is gathered and disseminated on the web [7].”
BackgroundInfluentialPublic awareness and attitudes towards search engine optimization
Lewandowski, D., & Schultheiß, S. (2022) · Behaviour and Information Technology · doi:10.1080/0144929x.2022.2056507 · Cited by 55 · see citing works
“This so-called data bias may lead to search engines reproducing what there is on the web and therefore leaning towards low-quality content (Baeza-Yates, 2018).”
BackgroundGao, R., & Shah, C. S. (2019) · ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR) · doi:10.1145/3341981.3344215 · Cited by 28 · see citing works
“IR systems should not only excel at helping users ind what they need, but also bear the social responsibility of being fair [2, 10, 13, 29, 30].”
BackgroundBaradaran, A. (2023) · Ai & Society · doi:10.1007/s00146-023-01771-5 · Cited by 33 · see citing works
Draws, T., Inel, O., Tintarev, N., Baden, C., & Timmermans, B. (2022) · Conference on Human Information Interaction and Retrieval · doi:10.1145/3498366.3505812 · Cited by 29 · see citing works
“Inspired by calls to combat bias on the web [7, 43], such research has explored user interactions with debated topics in web search [2, 17, 21, 25, 42, 46, 59], social media [37], and the web in general [36].”
BackgroundBias in Conversational Search: The Double-Edged Sword of the Personalized Knowledge Graph
Gerritse, E. J., Hasibi, F., & de Vries, A. P. (2020) · ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR) · doi:10.1145/3409256.3409834 · Cited by 26 · see citing works
“esponses or get along with users and provide them with what they ask. Such systems can fall into the trap of creating a vicious cycle of second-order bias (generated from people and algorithmic bias) [1] and then become their own enemy. An example of such a scenario is Microsoft’s AI chatbot that had to be taken down…”
Economic patterns in a world with artificial intelligence
Wagner, D. (2020) · Evolutionary and Institutional Economics Review · doi:10.1007/s40844-019-00157-x · Cited by 32 · see citing works
“In complex social environments with data from human society, these limitations can, for example, lead to bias in AI decision making (Baeza-Yates 2018; Boddington 2017).”
BackgroundEvery Colour You Are: Stance Prediction and Turnaround in Controversial Issues
Graells-Garrido, E., Baeza-Yates, R., & Lalmas, M. (2020) · Web Science Conference · doi:10.1145/3394231.3397907 · Cited by 31 · see citing works
Self-citationGenerative AI Search Engines as Arbiters of Public Knowledge: An Audit of Bias and Authority
Li, A., & Sinnamon, L. S. (2024) · Proceedings of the Association for Information Science and Technology · doi:10.1002/pra2.1021 · Cited by 19 · see citing works
“Many types and sources of bias influence SEs, including those that arise from system design and user interactions (Baeza-Yates, 2018; Friedman & Nissenbaum, 1996; Kay et al., 2015).”
Boonprakong, N., Chen, X., Davey, C. E., Tag, B., & Dingler, T. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580917 · Cited by 28 · see citing works
“However, given that cognitive biases normally happen without people being aware of them, it is challenging to objectively define and measure them [5].”
BackgroundHow to detect propaganda from social media? Exploitation of semantic and fine-tuned language models
Malik, M. S. I., Imran, T., & Jamjoom, M. M. (2023) · PeerJ Computer Science · doi:10.7717/peerj-cs.1248 · Cited by 30 · see citing works
“the article’s contents are propagandistic or not (Baeza-Yates, 2018). We have a prominent example of propaganda, which had been used in the 2016US Presidential elections (Mueller, 2018). The study by Rashkin et al. (2017a) introduced the word n-gram technique for propaganda detection, but the author(s) concluded that…”
BackgroundMethodInfluentialTowards fairness-aware multi-objective optimization
Yu, G., Ma, L., Wang, X., Du, W., Du, W., & Jin, Y. (2024) · Complex & Intelligent Systems · doi:10.1007/s40747-024-01668-w · Cited by 29 · see citing works
“As a consequence, ML is required to not only excel at aiding users in decision making but also bear the social responsibility of being fair [19], [20].”
InfluentialMisztal-Radecka, J., & Indurkhya, B. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102519 · Cited by 29 · see citing works
“…may also be diverse — for instance, as observed in Misztal-Radecka et al. (2019), the pool of available items may not address the diversity of user needs, or some algorithmic bias may be observed (Baeza-Yates, 2018; Bolukbasi et al., 2016; Caliskan et al., 2017; Graells-Garrido et al., 2015).”
Fairness for machine learning software in education: A systematic mapping study
Pham, N., Ngoc, H. P., & Nguyen‐Duc, A. (2024) · Journal of Systems and Software · doi:10.1016/j.jss.2024.112244 · Cited by 42 · see citing works
Understanding and Mitigating the Effect of Outliers in Fair Ranking
Sarvi, F., Heuss, M., Aliannejadi, M., Schelter, S., & de Rijke, M. (2021) · Web Search and Data Mining · doi:10.1145/3488560.3498441 · Cited by 28 · see citing works
Zhang, S., He, Z., Ye, Z.-Y., Sun, P., Ai, Q.-Y., Zhang, M., & Liu, Y.-Q. (2024) · Annual International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3626772.3657890 · Cited by 27 · see citing works
“Although these observed data usually contain abundant information, only considering existing information is not enough to gain a comprehensive understanding of users [1, 22].”
BackgroundZhu, Q., Lo, L. Y., Xia, M., Chen, Z., & Ma, X. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3555597 · Cited by 27 · see citing works
Khenissi, S., Boujelbene, M., & Nasraoui, O. (2020) · ACM Conference on Recommender Systems · doi:10.1145/3383313.3412260 · Cited by 27 · see citing works
“xploration approaches and their effectiveness in increasing human discovery 2 RELATED WORK The term bias in recommender systems has been used to represent a wide, but often related, variety of biases [9]. These include popularity bias, diversity bias, exposure bias, display bias, iterative bias, etc. We will therefore…”
BackgroundInfluentialResponsible AI in Farming: A Multi-Criteria Framework for Sustainable Technology Design
Mallinger, K., & Baeza-Yates, R. A. (2024) · Applied Sciences · doi:10.3390/app14010437 · Cited by 37 · see citing works
Self-citationAgarwal, A., & Agarwal, H. (2023) · AI and Ethics · doi:10.1007/s43681-023-00266-9 · Cited by 33 · see citing works
“AI system’s algorithm design, training, and validation phases can also contribute to bias in the overall system [36].”
Alaofi, M., Gallagher, L., Mckay, D., Saling, L. L., Sanderson, M., Scholer, F., Spina, D., & White, R. (2022) · Annual International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3477495.3531711 · Cited by 26 · see citing works
“…but also information scientists, social scientists, practicing librarians developing information literacy who could improve querying skills [87], and those developing policies and frameworks about issues including information interfaces [46], algorithmic bias [8], and misinformation [29].”
BackgroundWagner, D. N. (2020) · Telecommunications Policy · doi:10.1016/j.telpol.2020.101954 · Cited by 26 · see citing works
Counterfactual Online Learning to Rank
Zhuang, S., & Zuccon, G. (2020) · European Conference on Information Retrieval · doi:10.1007/978-3-030-45439-5_28 · Cited by 26 · see citing works
“According to position bias, users are more likely to click on top-ranked search results than those at the bottom of the SERP [2, 16, 18] : in CLTR this probability is referred to as propensity.”
BackgroundAuditing the representation of migrants in image web search results
Urman, A., Makhortykh, M., & Ulloa, R. (2022) · Humanities and Social Sciences Communications · doi:10.1057/s41599-022-01144-1 · Cited by 25 · see citing works
“These include technical limitations arising from the ways the data is sampled, societal/individual values affecting design decisions (Bozdag, 2013) and cognitive biases that arise from the user activity (Baeza-Yates, 2018).”
BackgroundFake opinion detection: how similar are crowdsourced datasets to real data?
Fornaciari, T., Cagnina, L., Rosso, P., & Poesio, M. (2020) · Language Resources and Evaluation · doi:10.1007/s10579-020-09486-5 · Cited by 16 · see citing works
“This is also called ‘activity bias’, or ‘wisdom of a few’ (Baeza-Yates, 2018).”
Investigating Searchers’ Mental Models to Inform Search Explanations
Thomas, P., Billerbeck, B., Craswell, N., & White, R. W. (2019) · ACM Transactions on Information Systems · doi:10.1145/3371390 · Cited by 20 · see citing works
An Investigation of Biases in Web Search Engine Query Suggestions
Bonart, M., Samokhina, A., Heisenberg, G., & Schaer, P. (2019) · Online information review (Print) · doi:10.1108/oir-11-2018-0341 · Cited by 24 · see citing works
“In a narrower statistical sense, errors in the estimation or sampling process cause a systematic deviation from the true unknown distribution (Baeza-Yates, 2018).”
- Authors
- Baeza-Yates, R.
- Affiliations
- Northeastern University (US)
- Corresponding author
- Baeza-Yates, Ricardo
- ORCID
- Baeza-Yates, Ricardo
- Venue
- Communications of the ACM
- Published
- 2018-05-23
- Volume
- 61
- Issue
- 6
- Pages
- 54-61
- ISSN
- 0001-0782 (ISSN-L)
- 1557-7317
- OpenAlex
- W2804927761
- Cited by
- 432
- References
- 0
- Citations by year
- 2026: 22 · 2025: 41 · 2024: 90 · 2023: 59 · 2022: 59 · 2021: 68 · 2020: 47 · 2019: 39 · 2018: 7
- FWCI
- 80.24
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Communications of the ACM (publishedVersion)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- algorithmic bias, algorithmic fairness, biased training data, web application
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
