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Individual fairness is an intuitive definition of algorithmic fairness that addresses some of the drawbacks of group fairness.
Metric Learning: A Survey
Kulis, B · 1935
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The Rotation of Eigenvectors by a Perturbation. III
Davis, C. and Kahan, W · 1970
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Weak convergence
Van Der Vaart, A. W. and Wellner, J. A · 1996
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Measuring Individual Differences in Implicit Cognition: The Implicit Association Test
Greenwald, A. G., McGhee, D. E., and Schwartz, J. L. K · 1998
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Asymptotic Statistics
van der Vaart, A. W · 1998
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Crowdsourcing Feature Discovery via Adaptively Chosen Comparisons
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Are Emily and Greg More Employable Than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination
Bertrand, M. and Mullainathan, S · 2004
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Mining and Summarizing Customer Reviews
Hu, M. and Liu, B · 2004
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Risk bounds for statistical learning
Massart, P., Nédélec, É., et al · 2006
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Learning Globally-Consistent Local Distance Functions for Shape-Based Image Retrieval and Classification
Frome, A., Singer, Y., Sha, F., and Malik, J · 2007
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Introduction to the non-asymptotic analysis of random matrices
Vershynin, R · 2010
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Dwork, C., Hardt, M., Pitassi, T., Reingold, O., and Zemel, R · 2011
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Low-dimensional embedding using adaptively selected ordinal data
Jamieson, K. G. and Nowak, R. D · 2011
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Implicit and explicit stigmatizing attitudes and stereotypes about depression
Monteith, L. L. and Pettit, J. W · 2011
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Adaptively Learning the Crowd Kernel
Tamuz, O., Liu, C., Belongie, S., Shamir, O., and Kalai, A. T · 2011
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Stochastic triplet embedding
van der Maaten, L. and Weinberger, K · 2012
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UCI machine learning repository
Bache, K. and Lichman, M · 2013
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A Survey on Metric Learning for Feature Vectors and Structured Data
Bellet, A., Habrard, A., and Sebban, M · 2013
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Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., and Dean, J · 2013
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Learning Fair Representations
Zemel, R., Wu, Y., Swersky, K., Pitassi, T., and Dwork, C · 2013
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Cost-Effective HITs for Relative Similarity Comparisons
Wilber, M. J., Kwak, I. S., and Belongie, S. J · 2014
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Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Radford, A., Metz, L., and Chintala, S · 2015
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Machine Bias
Angwin, J., Larson, J., Mattu, S., and Kirchner, L · 2016
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Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
Kannan, H., Kurakin, A., and Goodfellow, I · 2018
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Fairness Through Computationally-Bounded Awareness
Kim, M. P., Reingold, O., and Rothblum, G. N · 2018
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Analyze, Detect and Remove Gender Stereotyping from Bollywood Movies
Madaan, N., Mehta, S., Agrawaal, T., Malhotra, V., Aggarwal, A., Gupta, Y., and Saxena, M · 2018
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Learning Adversarially Fair and Transferable Representations
Madras, D., Creager, E., Pitassi, T., and Zemel, R · 2018
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Probably Approximately Metric-Fair Learning
Rothblum, G. N. and Yona, G · 2018
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Bolukbasi, T., Chang, K.-W., Zou, J., Saligrama, V., and Kalai, A · 2016
Cited alongside, same era.
On the (im)possibility of fairness
Friedler, S. A., Scheidegger, C., and Venkatasubramanian, S · 2016
Cited alongside, same era.
Finite Sample Prediction and Recovery Bounds for Ordinal Embedding
Jain, L., Jamieson, K., and Nowak, R · 2016
Cited alongside, same era.
Inherent Trade-Offs in the Fair Determination of Risk Scores
Kleinberg, J., Mullainathan, S., and Raghavan, M · 2016
Cited alongside, same era.
An Overview and Empirical Comparison of Distance Metric Learning Methods
Moutafis, P., Leng, M., and Kakadiaris, I. A · 2016
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Semantics derived automatically from language corpora contain human-like biases
Caliskan, A., Bryson, J. J., and Narayanan, A · 2017
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Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Chouldechova, A · 2017
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A Tutorial on Distance Metric Learning: Mathematical Foundations, Algorithms and Software
Suárez, J. L., García, S., and Herrera, F · 2018
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Understanding the origins of bias in word embeddings
Brunet, M.-E., Alkalay-Houlihan, C., Anderson, A., and Zemel, R · 2019
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Attenuating bias in word vectors
Dev, S. and Phillips, J · 2019
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Counterfactual fairness in text classification through robustness
Garg, S., Perot, V., Limtiaco, N., Taly, A., Chi, E. H., and Beutel, A · 2019
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Metric Learning for Individual Fairness
Ilvento, C · 2019
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Eliciting and Enforcing Subjective Individual Fairness
Jung, C., Kearns, M., Neel, S., Roth, A., Stapleton, L., and Wu, Z. S · 2019
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iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making
Lahoti, P., Gummadi, K. P., and Weikum, G · 2019
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What’s in a Name? Reducing Bias in Bios without Access to Protected Attributes
Romanov, A., De-Arteaga, M., Wallach, H., Chayes, J., Borgs, C., Chouldechova, A., Geyik, S., Kenthapadi, K., Rumshisky, A., and Kalai, A. T · 2019
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Apple Card Investigated After Gender Discrimination Complaints
Vigdor, N · 2019
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An Empirical Study on Learning Fairness Metrics for COMPAS Data with Human Supervision
Wang, H., Grgic-Hlaca, N., Lahoti, P., Gummadi, K. P., and Weller, A · 2019
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Gender bias in contextualized word embeddings
Zhao, J., Wang, T., Yatskar, M., Cotterell, R., Ordonez, V., and Chang, K.-W · 2019
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Training individually fair ML models with sensitive subspace robustness
Yurochkin, M., Bower, A., and Sun, Y · 2020
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