The DOI matches its Crossref record; 85 of the 85 references it cites were found in a registry or an index.
Investigating the Impact of Gender on Rank in Resume Search Engines
Open accessAbstract
In this work we investigate gender-based inequalities in the context of resume search engines, which are tools that allow recruiters to proactively search for candidates based on keywords and filters. If these ranking algorithms take demographic features into account (directly or indirectly), they may produce rankings that disadvantage some candidates. We collect search results from Indeed, Monster, and CareerBuilder based on 35 job titles in 20 U. S. cities, resulting in data on 855K job candidates. Using statistical tests, we examine whether these search engines produce rankings that exhibit two types of indirect discrimination: individual and group unfairness. Furthermore, we use controlled experiments to show that these websites do not use inferred gender of candidates as explicit features in their ranking algorithms.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Usually, a biased system generates the biased output, and the uncontrolled biases can lead to the uneven distribution of opportunities and resources [58, 78], exacerbating the “ 𝐸𝑐ℎ𝑜 𝐶ℎ𝑎𝑚𝑏𝑒𝑟 ” or “ 𝐹𝑖𝑙𝑡𝑒𝑟 𝐵𝑢𝑏𝑏𝑙𝑒𝑠 ” effects [86, 87].”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [58]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Google, Twitter, and Pinterest all provide similar features; these are called Customer Match[7], Tailored Audiences, and Customer Lists[61], respectively.”
From Discrimination through Optimization (2019) · cites this paper as [7]
Experimental — accuracy not yet measuredIn the citing paper
“Automated interviews, including the use of speech and facial recognition software, has been shown to perform poorly, particularly with regards to women of colour [15,16].”
From What does it mean to 'solve' the problem of discrimination in hiring?: social, technical and legal perspectives from the UK on automated hiring systems (2020) · cites this paper as [16]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Cumulated gain-based evaluation of IR techniques
Järvelin, K., & Kekäläinen, J. (2002) · ACM Transactions on Information Systems · doi:10.1145/582415.582418 · Cited by 4,841
Bertrand, M., & Mullainathan, S. (2004) · American Economic Review · doi:10.1257/0002828042002561 · Cited by 4,504
MatchIt : Nonparametric Preprocessing for Parametric Causal Inference
Ho, D. E., Imai, K., King, G., & Stuart, E. A. (2011) · Journal of Statistical Software · doi:10.18637/jss.v042.i08 · Cited by 4,385
Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference
Ho, D. E., Imai, K., King, G., & Stuart, E. A. (2007) · Political Analysis · doi:10.1093/pan/mpl013 · Cited by 4,285
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., & Zemel, R. (2012) · Proceedings of the 3rd Innovations in Theoretical Computer Science Conference · doi:10.1145/2090236.2090255 · Cited by 3,501
Barocas, S., & Selbst, A. D. (2014) · doi:10.2139/ssrn.2477899 · Cited by 3,048
Semantics derived automatically from language corpora contain human-like biases
Caliskan, A., Bryson, J. J., & Narayanan, A. (2017) · Science · doi:10.1126/science.aal4230 · Cited by 2,887
Certifying and Removing Disparate Impact
Feldman, M., Friedler, S. A., Moeller, J., Scheidegger, C., & Venkatasubramanian, S. (2015) · Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2783258.2783311 · Cited by 1,827
IR evaluation methods for retrieving highly relevant documents
Järvelin, K., & Kekäläinen, J. (2017) · ACM SIGIR Forum · doi:10.1145/3130348.3130374 · Cited by 1,460
(2013) · Cited by 1,126
Inoculating the Public against Misinformation about Climate Change
van der Linden, S., Leiserowitz, A., Rosenthal, S., & Maibach, E. (2017) · Global Challenges · doi:10.1002/gch2.201600008 · Cited by 1,058
Predicting clicks: estimating the click-through rate for new ads
Richardson, M., Dominowska, E., & Ragno, R. (2007) · Proceedings of the 16th international conference on World Wide Web · doi:10.1145/1242572.1242643 · Cited by 1,049
Racial Discrimination in the Sharing Economy: Evidence from a Field Experiment
Edelman, B. G., Luca, M., & Svirsky, D. (2015) · American Economic Journal Applied Economics · doi:10.1257/app.20160213 · Cited by 1,042
Racial Discrimination in the Sharing Economy: Evidence from a Field Experiment
Edelman, B. G., Luca, M., & Svirsky, D. (2015) · SSRN Electronic Journal · doi:10.2139/ssrn.2701902 · Cited by 999
IR evaluation methods for retrieving highly relevant documents
Järvelin, K., & Kekäläinen, J. (2000) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/345508.345545 · Cited by 979
An experimental comparison of click position-bias models
Craswell, N., Zoeter, O. R., Taylor, M. J. H., & Ramsey, B. (2008) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/1341531.1341545 · Cited by 947
Three naive Bayes approaches for discrimination-free classification.
Calders, T., & Verwer, S. (2010) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-010-0190-x · Cited by 764
Eye-tracking analysis of user behavior in WWW search
Granka, L. A., Joachims, T., & Gay, G. K. (2004) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/1008992.1009079 · Cited by 701
Fairness-Aware Classifier with Prejudice Remover Regularizer
Kamishima, T., Akaho, S., Asoh, H., & Sakuma, J. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33486-3_3 · Cited by 694
Automated Experiments on Ad Privacy Settings: A Tale of Opacity, Choice, and Discrimination
Datta, A., Tschantz, M. C., & Datta, A. (2015) · Proceedings on Privacy Enhancing Technologies · doi:10.1515/popets-2015-0007 · Cited by 676
Discrimination-aware data mining
Pedreshi, D., Ruggieri, S., & Turini, F. (2008) · Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1401890.1401959 · Cited by 676
Nonparametric Statistics: A Step‐by‐Step Approach
Richardson, A. (2015) · International Statistical Review · doi:10.1111/insr.12095_3 · Cited by 641
The search engine manipulation effect (SEME) and its possible impact on the outcomes of elections
Epstein, R., & Robertson, R. E. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1419828112 · Cited by 640
Filho, S. L., & Kane, B. T. (2016) · National Conference on Artificial Intelligence · Cited by 566
MARKED: Race, Crime, and Finding Work in an Era of Mass Incarceration
Ylang, N. (2015) · Sage eBooks · doi:10.4135/9781452206905.n425 · Cited by 559
Unbiased Learning-to-Rank with Biased Feedback
Joachims, T., Swaminathan, A., & Schnabel, T. (2017) · Proceedings of the Tenth ACM International Conference on Web Search and Data Mining · doi:10.1145/3018661.3018699 · Cited by 531
Unequal Representation and Gender Stereotypes in Image Search Results for Occupations
Kay, M., Matuszek, C., & Munson, S. A. (2015) · Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems · doi:10.1145/2702123.2702520 · Cited by 509
Sweeney, L. (2013) · Queue · doi:10.1145/2460276.2460278 · Cited by 477
Building Classifiers with Independency Constraints
Calders, T., Kamiran, F., & Pechenizkiy, M. (2009) · 2009 IEEE International Conference on Data Mining Workshops · doi:10.1109/icdmw.2009.83 · Cited by 468 · cited 2×
“Thus, most existing fair classification systems optimize for individual [50, 53, 66, 52, 20] or group fairness [48, 9, 49, 11, 96, 51, 10, 34, 24].”
BackgroundAge discrimination in simulated employment contexts: An integrative analysis.
Finkelstein, L. M., Burke, M. J., & Raju, M. S. (1995) · Journal of Applied Psychology · doi:10.1037/0021-9010.80.6.652 · Cited by 448
Classifying without discriminating
Kamiran, F., & Calders, T. (2009) · 2009 2nd International Conference on Computer, Control and Communication · doi:10.1109/ic4.2009.4909197 · Cited by 409
Measuring price discrimination and steering on e-commerce web sites
Hannak, A., Soeller, G., Lazer, D., Mislove, A., & Wilson, C. (2014) · Proceedings of the 2014 Conference on Internet Measurement Conference · doi:10.1145/2663716.2663744 · Cited by 405 · cited 1×
“Prior algorithm audits have examined search engines [35, 58], online maps [84], social networks [23], e-commerce [69, 70, 36, 15], and online advertising [33, 61].”
BackgroundSelf-citationMeasuring personalization of web search
Hannak, A., Sapiezynski, P., Molavi Kakhki, A., Krishnamurthy, B., Lazer, D., Mislove, A., & Wilson, C. (2013) · Proceedings of the 22nd international conference on World Wide Web · doi:10.1145/2488388.2488435 · Cited by 396
Self-citationFairness-aware Learning through Regularization Approach
Kamishima, T., Akaho, S., & Sakuma, J. (2011) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2011.83 · Cited by 383
Measuring Fairness in Ranked Outputs
Yang, K., & Stoyanovich, J. (2017) · Proceedings of the 29th International Conference on Scientific and Statistical Database Management · doi:10.1145/3085504.3085526 · Cited by 378
Nonparametric Statistics : A Step-by-Step Approach
Corder, G. W., & Foreman, D. I. (2014) · CERN Document Server (European Organization for Nuclear Research) · Cited by 350
Marked: Race, Crime, and Finding Work in an Era of Mass Incarceration
Gatson, S. N. (2008) · Contemporary Sociology A Journal of Reviews · doi:10.1177/009430610803700516 · Cited by 348
RISK, RACE, AND RECIDIVISM: PREDICTIVE BIAS AND DISPARATE IMPACT*
SKEEM, J. L., & LOWENKAMP, C. T. (2016) · Criminology · doi:10.1111/1745-9125.12123 · Cited by 317
A Methodology for Direct and Indirect Discrimination Prevention in Data Mining
Hajian, S., & Domingo-Ferrer, J. (2013) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2012.72 · Cited by 312
An Empirical Analysis of Algorithmic Pricing on Amazon Marketplace
Chen, L., Mislove, A. E., & Wilson, C. (2016) · International World Wide Web Conference (WWW) · doi:10.1145/2872427.2883089 · Cited by 309
Self-citationDiscrimination Aware Decision Tree Learning
Kamiran, F., Calders, T., & Pechenizkiy, M. (2010) · 2010 IEEE International Conference on Data Mining · doi:10.1109/icdm.2010.50 · Cited by 303
Decision Theory for Discrimination-Aware Classification
Kamiran, F., Karim, A., & Zhang, X. (2012) · 2012 IEEE 12th International Conference on Data Mining · doi:10.1109/icdm.2012.45 · Cited by 300
Bias in Online Freelance Marketplaces: Evidence from TaskRabbit and Fiverr
Hannák, A., Wagner, C., Garcia, D., Mislove, A., Strohmaier, M., & Wilson, C. (2017) · Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing · doi:10.1145/2998181.2998327 · Cited by 286 · cited 6×
“This is a common limitation of work that uses observational datasets to examine gender biases in online contexts [55, 37].”
BackgroundResultInfluentialSelf-citationUse of geocoding and surname analysis to estimate race and ethnicity.
Fiscella, K., & Fremont, A. M. (2006) · Health Services Research · doi:10.1111/j.1475-6773.2006.00551.x · Cited by 272
Sexual orientation discrimination in hiring
Weichselbaumer, D. (2003) · Labour Economics · doi:10.1016/s0927-5371(03)00074-5 · Cited by 266
k-NN as an implementation of situation testing for discrimination discovery and prevention
Luong, B. T., Ruggieri, S., & Turini, F. (2011) · Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2020408.2020488 · Cited by 263
On the (im)possibility of fairness
Friedler, S. A., Scheidegger, C., & Venkatasubramanian, S. (2016) · arXiv · doi:10.48550/arxiv.1609.07236 · Cited by 247
Racial and Gender Discrimination in Transportation Network Companies
Ge, Y., Knittel, C. R., MacKenzie, D., & Zoepf, S. M. (2016) · National Bureau of Economic Research · doi:10.3386/w22776 · Cited by 246
Detecting price and search discrimination on the internet
Mikians, J., Gyarmati, L., Erramilli, V., & Laoutaris, N. (2012) · Proceedings of the 11th ACM Workshop on Hot Topics in Networks · doi:10.1145/2390231.2390245 · Cited by 244
An eye tracking study of the effect of target rank on web search
Guan, Z., & Cutrell, E. B. (2007) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1240624.1240691 · Cited by 243
It's a Man's Wikipedia? Assessing Gender Inequality in an Online Encyclopedia
Wagner, C., García, D., Jadidi, M., & Strohmaier, M. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v9i1.14628 · Cited by 232
Lam, S. K., Uduwage, A., Dong, Z., Sen, S., Musicant, D. R., Terveen, L., & Riedl, J. (2011) · International Symposium on Wikis and Open Collaboration (WikiSym) · doi:10.1145/2038558.2038560 · Cited by 198
Measuring Employment Discrimination through Controlled Experiments
Bendick, M., Jackson, C. W., & Reinoso, V. A. (1994) · The Review of Black Political Economy · doi:10.1007/bf02895739 · Cited by 173
Are people biased in their use of search engines?
Keane, M. T., O’Brien, M., & Smyth, B. (2008) · Communications of the ACM · doi:10.1145/1314215.1314224 · Cited by 170
Challenges in measuring online advertising systems
Guha, S., Cheng, B., & Francis, P. (2010) · Proceedings of the 10th ACM SIGCOMM conference on Internet measurement · doi:10.1145/1879141.1879152 · Cited by 169
What's in a Name? Using First Names as Features for Gender Inference in Twitter
Liu, W. J., & Ruths, D. A. (2013) · National Conference on Artificial Intelligence · Cited by 165
Controlling Attribute Effect in Linear Regression
Calders, T., Karim, A., Kamiran, F., Ali, W., & Zhang, X.-L. (2013) · 2013 IEEE 13th International Conference on Data Mining · doi:10.1109/icdm.2013.114 · Cited by 162
Quantifying Search Bias: Investigating Sources of Bias for Political Searches in Social Media
Kulshrestha, J., Eslami, M., Messias, J., Zafar, M. B., Ghosh, S., Gummadi, K. P., & Karahalios, K. (2017) · Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing · doi:10.1145/2998181.2998321 · Cited by 148
Quantifying Search Bias: Investigating Sources of Bias for Political Searches in Social MediaPreprint
Kulshrestha, J., Eslami, M., Messias, J., Zafar, M. B., Ghosh, S., Gummadi, K. P., & Karahalios, K. G. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.01347 · Cited by 134
Automated Experiments on Ad Privacy Settings: A Tale of Opacity, Choice, and DiscriminationPreprint
Datta, A. J., Tschantz, M. C., & Datta, A. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1408.6491 · Cited by 129
Handling Conditional Discrimination
liobaite, I., Kamiran, F., & Calders, T. (2011) · 2011 IEEE 11th International Conference on Data Mining · doi:10.1109/icdm.2011.72 · Cited by 125
Kliman-Silver, C., Hannák, A., Lazer, D. M. J., Wilson, C., & Mislove, A. E. (2015) · ACM Internet Measurement Conference (IMC) · doi:10.1145/2815675.2815714 · Cited by 125
Self-citationAllam, A. A., Schulz, P. J., & Nakamoto, K. (2014) · Journal of Medical Internet Research · doi:10.2196/jmir.2642 · Cited by 123
Inferring Gender from Names on the Web
Karimi, F., Wagner, C., Lemmerich, F., Jadidi, M., & Strohmaier, M. (2016) · International World Wide Web Conference (WWW) · doi:10.1145/2872518.2889385 · Cited by 122
Avoiding the South Side and the Suburbs
Thebault-Spieker, J., Terveen, L., & Hecht, B. J. (2015) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2675133.2675278 · Cited by 121
Gender Bias in Wikipedia and Britannica
Reagle, J., & Rhue, L. A. (2011) · International journal of communication · Cited by 119
Gouvier, W. D., Sytsma-Jordan, S. E., & Mayville, S. B. (2003) · Rehabilitation Psychology · doi:10.1037/0090-5550.48.3.175 · Cited by 117
The pursuit of fairness: a history of affirmative action
(2005) · Choice Reviews Online · doi:10.5860/choice.42-3594 · Cited by 109
What’s in a Name: A Study of Names, Gender Inference, and Gender Behavior in Facebook
Tang, C., Ross, K. W., Saxena, N., & Chen, R. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-20244-5_33 · Cited by 99
Inferring Gender from Names on the Web: A Comparative Evaluation of Gender Detection MethodsPreprint
Karimi, F., Wagner, C., Lemmerich, F., Jadidi, M., & Strohmaier, M. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.04322 · Cited by 91
XRay: Enhancing the Web's Transparency with Differential CorrelationPreprint
Lécuyer, M., Ducoffe, G., Lan, F., Papancea, A., Petsios, T., Spahn, R., Chaintreau, A., & Geambasu, R. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1407.2323 · Cited by 87
Crowd-assisted search for price discrimination in e-commerce: first results
Mikians, J., Gyarmati, L., Erramilli, V., & Laoutaris, N. (2013) · Proceedings of the ninth ACM conference on Emerging networking experiments and technologies · doi:10.1145/2535372.2535415 · Cited by 82
Risk, Race, & Recidivism: Predictive Bias and Disparate ImpactPreprint
Skeem, J., & Lowenkamp, C. T. (2016) · SSRN Electronic Journal · doi:10.2139/ssrn.2687339 · Cited by 73
The Effect of Population and "Structural" Biases on Social Media-based Algorithms
Johnson, I. L., McMahon, C., Schöning, J., & Hecht, B. J. (2017) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3025453.3026015 · Cited by 62
Ludolph, R. A., Allam, A., & Schulz, P. J. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.5430 · Cited by 58
Making Sense of Conflicting Science Information
Novin, A., & Meyers, E. (2017) · Conference on Human Information Interaction and Retrieval · doi:10.1145/3020165.3020185 · Cited by 58
Learning Query and Document Relevance from a Web-scale Click Graph
Jiang, S., Hu, Y., Kang, C., Daly, T., Yin, D., Chang, Y., & Zhai, C. (2016) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/2911451.2911531 · Cited by 46
Comparing Pointwise and Listwise Objective Functions for Random-Forest-Based Learning-to-Rank
Ibrahim, M., & Carman, M. (2016) · ACM Transactions on Information Systems · doi:10.1145/2866571 · Cited by 41
Torvik, V. I., & Agarwal, S. (2016) · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign) · Cited by 39
Eslami, M., Aleyasen, A., Karahalios, K. G., Hamilton, K., & Sandvig, C. (2015) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2685553.2702690 · Cited by 37
Measuring Discrimination by Direct Experimental Methods: Seeking Gunsmoke
Riach, P. A., & Rich, J. (1991) · Journal of Post Keynesian Economics · doi:10.1080/01603477.1991.11489889 · Cited by 31
MapWatch: Detecting and Monitoring International Border Personalization on Online Maps
Soeller, G., Karahalios, K., Sandvig, C., & Wilson, C. (2016) · The Web Conference · doi:10.1145/2872427.2883016 · Cited by 23
Self-citationA search engine approach to estimating temporal changes in gender orientation of first names
Smith, B. N., Singh, M. K., & Torvik, V. I. (2013) · ACM/IEEE Joint Conference on Digital Libraries · doi:10.1145/2467696.2467720 · Cited by 22
de Sá, C. C. A., Gonçalves, M. A., de Sousa, D. X., & Salles, T. (2016) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/2911451.2911540 · Cited by 8
Anderson, R. (2000) · doi:10.15760/etd.5335 · Cited by 4
Research works printed but not linked: 11
- Quoctrung Bui. 2014. The Most Common Jobs For The Rich, Middle Class And Poor. NPR. (October 2014). http://n.pr/2iSViU0.
- CEB 2011. Most Common Job Titles Posted Online. CEB. (August 2011). http://bit.ly/2iSYb9h.
- Steven Greenhouse. 2013. Supreme Court Raises Bar to Prove Job Discrimination. New York Times. (June 2013). http://nyti.ms/2ppuG3w.
- Faisal Kamiran and Toon Calders. 2010. Classification with No Discrimination by Preferential Sampling. In Proc. of Machine Learning Conference of Belgium and The Netherlands.
- Pauline T. Kim. 2017. Data-Driven Discrimination at Work. William & Mary Law Review 58 (2017).
- Thomas Lee. 2017. LinkedIn, HiQ spat presents big questions for freedom, innovation. San Francisco Chronicle. (July 2017). http://www.sfchronicle.com/ business/article/LinkedIn-HiQ-spat-presentsbig-questions-for-11274133.php.
- Gabriel Magno, Camila Souza Araujo, Wagner Meira Jr., and Virgílio A. F. Almeida. 2016. Stereotypes in Search Engine Answers: Local or Global? CoRR abs/1609.05413 (2016). · arXiv
- Devah Pager and Hana Shepherd. 2008. The sociology of discrimination: Racial discrimination in employment, housing, credit, and consumer markets. Annual review of sociology 34 (2008), 181.
- Christian Sandvig, Kevin Hamilton, Karrie Karahalios, and Cedric Langbort. 2014. Auditing algorithms: Research methods for detecting discrimination on internet platforms. In Proceedings of "Data and Discrimination: Converting Critical Concerns into Productive Inquiry", a preconference at the 64th Annual Meeting of the International Communication Association.
- Latanya Sweeney. 2013. Discrimination in Online Ad Delivery. (2013). http://ssrn.com/abstract=2208240. · DOI
- 10.5555/1996686.1996731 · DOI
Cited by
Sorted by citations
Köchling, A., & Wehner, M. C. (2020) · Business Research · doi:10.1007/s40685-020-00134-w · Cited by 511
Roles for computing in social change
Abebe, R., Barocas, S., Kleinberg, J. M., Levy, K. E. C., Raghavan, M., & Robinson, D. G. (2020) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3351095.3372871 · Cited by 341
Discrimination through Optimization
Ali, M., Sapiezynski, P., Bogen, M., Korolova, A., Mislove, A., & Rieke, A. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359301 · Cited by 268
“s “black boxes” and observe their reactions to di˛erent inputs. Among most notable results, researchers have shown price discrimination in online retail sites [40], gender discrimination in job sites [15, 41], stereotypical gender roles re-enforced by online translation services [12] and image search [43], disparate p…”
BackgroundMeasuring Misinformation in Video Search Platforms: An Audit Study on YouTube
Hussein, E., Juneja, P., & Mitra, T. (2020) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3392854 · Cited by 268
Sánchez-Monedero, J., Dencik, L., & Edwards, L. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372849 · Cited by 196
Metadata
- Authors
- Chen, L., Ma, R., Hannák, A., & Wilson, C.
- Affiliations
- Northeastern University (US)
- Rutgers, The State University of New Jersey (US)
- Central European University - Budapest Campus (HU)
- Venue
- Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2018-04-21
- Pages
- 1-14
- OpenAlex
- W2796402109
- Cited by
- 155
- References
- 85
- Citations by year
- 2026: 7 · 2025: 10 · 2024: 21 · 2023: 19 · 2022: 30 · 2021: 29 · 2020: 16 · 2019: 21 · 2018: 2
- FWCI
- 30.43
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- Funders
- NSF (IIS-1408345, IIS-1553088)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Sociology and Political Science › Names, Identity, and Discrimination Research
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Keywords
- gender discrimination, job search engines, algorithmic fairness, indirect discrimination, candidate ranking, gender inference, Indeed, monsters, recruitment bias
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
