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Existing fair ranking systems, especially those designed to be demographically fair, assume that accurate demographic information about individuals is available to the ranking algorithm.
Fairness in Recommendation Ranking through Pairwise Comparisons. In
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Cumulated gain-based evaluation of IR techniques
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Usability 101: introduction to usability. Jakob Nielsen’s Alertbox
Jakob Nielsen. 2003 · 2003
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Planetary-Scale Views on a Large Instant-Messaging Network. In
Jure Leskovec and Eric Horvitz. 2008 · 2008
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Evaluation in information retrieval
Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze. 2009 · 2009
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40 years of boxplots
Lisa Stryjewski. 2010 · 2010
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Fairness through awareness. In
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel. 2012 · 2012
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Fairness-aware classifier with prejudice remover regularizer. In
Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, and Jun Sakuma. 2012 · 2012
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Tackling the problem of classification with noisy data using multiple classifier systems: Analysis of the performance and robustness
José A Sáez, Mikel Galar, JuliáN Luengo, and Francisco Herrera. 2013 · 2013
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Using the Bayesian Improved Surname Geocoding Method (BISG) to create a working classification of race and ethnicity in a diverse managed care population: a validation study
Dzifa Adjaye-Gbewonyo, Robert A Bednarczyk, Robert L Davis, and Saad B Omer. 2014 · 2014
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Using publicly available information to proxy for unidentified race and ethnicity
Consumer Financial Protection Bureau. 2014 · 2014
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Convolutional Neural Networks for Sentence Classification. In
Yoon Kim. 2014 · 2014
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Deepface: Closing the gap to human-level performance in face verification. In
Yaniv Taigman, Ming Yang, Marc’Aurelio Ranzato, and Lior Wolf. 2014 · 2014
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Unequal Representation and Gender Stereotypes in Image Search Results for Occupations. In
Matthew Kay, Cynthia Matuszek, and Sean A. Munson. 2015 · 2015
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Big Data’s Disparate Impact
Solon Barocas and Andrew D. Selbst. 2016 · 2016
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Man is to computer programmer as woman is to homemaker? debiasing word embeddings. In
Tolga Bolukbasi, Kai-Wei Chang, James Y Zou, Venkatesh Saligrama, and Adam T Kalai. 2016 · 2016
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Equality of opportunity in supervised learning. In
Moritz Hardt, Eric Price, and Nati Srebro. 2016 · 2016
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" Why should I trust you?" Explaining the predictions of any classifier. In
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
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A convex framework for fair regression
Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern, Seth Neel, and Aaron Roth. 2017 · 2017
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Bias in Online Freelance Marketplaces: Evidence from TaskRabbit and Fiverr. In
Anikó Hannák, Claudia Wagner, David Garcia, Alan Mislove, Markus Strohmaier, and Christo Wilson. 2017 · 2017
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Fairness in reinforcement learning. In
Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern, and Aaron Roth. 2017 · 2017
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Quantifying Search Bias: Investigating Sources of Bias for Political Searches in Social Media. In
Juhi Kulshrestha, Motahhare Eslami, Johnnatan Messias, Muhammad Bilal Zafar, Saptarshi Ghosh, Krishna P. Gummadi, and Karrie Karahalios. 2017 · 2017
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A Unified Approach to Interpreting Model Predictions
Scott M Lundberg and Su-In Lee. 2017 · 2017
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An intelligence in our image: The risks of bias and errors in artificial intelligence
Osonde A Osoba and William Welser IV. 2017 · 2017
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Measuring fairness in ranked outputs. In
Ke Yang and Julia Stoyanovich. 2017 · 2017
Cited alongside, same era.
Fairness constraints: Mechanisms for fair classification. In
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rogriguez, and Krishna P Gummadi. 2017 · 2017
Cited alongside, same era.
Fa* ir: A fair top-k ranking algorithm. In
Meike Zehlike, Francesco Bonchi, Carlos Castillo, Sara Hajian, Mohamed Megahed, and Ricardo Baeza-Yates. 2017 · 2017
Cited alongside, same era.
Rachel KE Bellamy, Kuntal Dey, Michael Hind, Samuel C Hoffman, Stephanie Houde, Kalapriya Kannan, Pranay Lohia, Jacquelyn Martino, Sameep Mehta, Aleksandra Mojsilovic, et al · 2018
Cited alongside, same era.
Equity of attention: Amortizing individual fairness in rankings. In
Asia J Biega, Krishna P Gummadi, and Gerhard Weikum. 2018 · 2018
Cited alongside, same era.
A survey on bias and fairness in machine learning
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan. 2019 · 2019
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Public Sphere 2.0: Targeted Commenting in Online News Media. In
Ankan Mullick, Sayan Ghosh, Ritam Dutt, Avijit Ghosh, and Abhijnan Chakraborty. 2019 · 2019
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Dissecting racial bias in an algorithm used to manage the health of populations
Ziad Obermeyer, Brian Powers, Christine Vogeli, and Sendhil Mullainathan. 2019 · 2019
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Auditing Autocomplete: Recursive Algorithm Interrogation and Suggestion Networks. In
Ronald E. Robertson, Shan Jiang, David Lazer, and Christo Wilson. 2019 · 2019
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Quantifying the Impact of User Attentionon Fair Group Representation in Ranked Lists. In
Piotr Sapiezynski, Wesley Zeng, Ronald E Robertson, Alan Mislove, and Christo Wilson. 2019 · 2019
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Gender shades: Intersectional accuracy disparities in commercial gender classification. In
Joy Buolamwini and Timnit Gebru. 2018 · 2018
Cited alongside, same era.
Ranking with Fairness Constraints. In
L Elisa Celis, Damian Straszak, and Nisheeth K Vishnoi. 2018 · 2018
Cited alongside, same era.
Investigating the Impact of Gender on Rank in Resume Search Engines. In
Le Chen, Ruijun Ma, Anikó Hannák, and Christo Wilson. 2018 · 2018
Cited alongside, same era.
I Vote For—How Search Informs Our Choice of Candidate
Nicholas Diakopoulos, Daniel Trielli, Jennifer Stark, and Sean Mussenden. 2018 · 2018
Cited alongside, same era.
Non-discriminatory machine learning through convex fairness criteria. In
Naman Goel, Mohammad Yaghini, and Boi Faltings. 2018 · 2018
Cited alongside, same era.
Causal reasoning for algorithmic fairness
Joshua R Loftus, Chris Russell, Matt J Kusner, and Ricardo Silva. 2018 · 2018
Cited alongside, same era.
Investigating the Effects of Google’s Search Engine Result Page in Evaluating the Credibility of Online News Sources. In
Emma Lurie and Eni Mustafaraj. 2018 · 2018
Cited alongside, same era.
The what-if tool: Interactive probing of machine learning models
James Wexler, Mahima Pushkarna, Tolga Bolukbasi, Martin Wattenberg, Fernanda Viégas, and Jimbo Wilson. 2019 · 2019
Later among the works it cites.
Measuring discrepancies in Airbnb guest acceptance rates using anonymized demographic data
Sid Basu, Ruthie Berman, Adam Bloomston, John Campbell, Anne Diaz, Nanako Era, Benjamin Evans, Sukhada Palkar, and Skyler Wharton. 2020 · 2020
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Awareness in practice: tensions in access to sensitive attribute data for antidiscrimination. In
Miranda Bogen, Aaron Rieke, and Shazeda Ahmed. 2020 · 2020
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Fair Classification with Noisy Protected Attributes
L Elisa Celis, Lingxiao Huang, and Nisheeth K Vishnoi. 2020 · 2020
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Implicit Diversity in Image Summarization
L Elisa Celis and Vijay Keswani. 2020 · 2020
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Evaluating stochastic rankings with expected exposure. In
Fernando Diaz, Bhaskar Mitra, Michael D Ekstrand, Asia J Biega, and Ben Carterette. 2020 · 2020
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Principled Artificial Intelligence: Mapping Consensus in Ethical and Rights-Based Approaches to Principles for AI
Jessica Fjeld, Nele Achten, Hannah Hilligoss, Adam Nagy, and Madhulika Srikumar. 2020 · 2020
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Garbage in, garbage out? do machine learning application papers in social computing report where human-labeled training data comes from?. In
R Stuart Geiger, Kevin Yu, Yanlai Yang, Mindy Dai, Jie Qiu, Rebekah Tang, and Jenny Huang. 2020 · 2020
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Towards a critical race methodology in algorithmic fairness. In
Alex Hanna, Emily Denton, Andrew Smart, and Jamila Smith-Loud. 2020 · 2020
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Terms of inclusion: Data, discourse, violence
Anna Lauren Hoffmann. 2020 · 2020
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The Diversity–Innovation Paradox in Science
Bas Hofstra, Vivek V Kulkarni, Sebastian Munoz-Najar Galvez, Bryan He, Dan Jurafsky, and Daniel A McFarland. 2020 · 2020
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What’s Sex Got To Do With Machine Learning
Lily Hu and Issa Kohler-Hausmann. 2020 · 2020
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Measuring Misinformation in Video Search Platforms: An Audit Study on YouTube
Eslam Hussein, Prerna Juneja, and Tanushree Mitra. 2020 · 2020
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Lessons from archives: Strategies for collecting sociocultural data in machine learning. In
Eun Seo Jo and Timnit Gebru. 2020 · 2020
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The ‘Fairness Doctrine’ Lives on? Theorizing about the Algorithmic News Curation of Google’s Top Stories. In
Anna Kawakami, Khonzoda Umarova, Dongchen Huang, and Eni Mustafaraj. 2020 · 2020
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Mitigating Bias in Set Selection with Noisy Protected Attributes
Anay Mehrotra and L Elisa Celis. 2020 · 2020
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Controlling Fairness and Bias in Dynamic Learning-to-Rank
Marco Morik, Ashudeep Singh, Jessica Hong, and Thorsten Joachims. 2020 · 2020
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Comparing Fair Ranking Metrics
Amifa Raj, Connor Wood, Ananda Montoly, and Michael D Ekstrand. 2020 · 2020
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LightFace: A Hybrid Deep Face Recognition Framework. In
Sefik Ilkin Serengil and Alper Ozpinar. 2020 · 2020
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Reducing disparate exposure in ranking: A learning to rank approach. In
Meike Zehlike and Carlos Castillo. 2020 · 2020
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What We Can’t Measure, We Can’t Understand: Challenges to Demographic Data Procurement in the Pursuit of Fairness. In
McKane Andrus, Elena Spitzer, Jeffrey Brown, and Alice Xiang. 2021 · 2021
Closest in time.
Characterizing Intersectional Group Fairness with Worst-Case Comparisons
Avijit Ghosh, Lea Genuit, and Mary Reagan. 2021 · 2021
Closest in time.