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Mitigating bias in machine learning systems requires refining our understanding of bias propagation pathways: from societal structures to large-scale data to trained models to impact on society.
Bootstrap methods: another look at the jackknife
Efron, B · 1992
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Statistical modeling: The two cultures
Breiman, L · 2001
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Do instrumental variables belong in propensity scores?
Bhattacharya, J. and Vogt, W. B · 2007
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The fabric of internalized sexism
Bearman, S., Korobov, N., and Thorne, A · 2009
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On a class of bias-amplifying variables that endanger effect estimates
Pearl, J · 2010
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Invited commentary: Understanding bias amplification
Pearl, J · 2011
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Fairness through awareness
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., and Zemel, R · 2012
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
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Microsoft COCO: Common objects in context
Lin, T.-Y., Maire, M., Belongie, S., Bourdev, L., Girshick, R., Hays, J., Perona, P., Ramanan, D., Zitnick, C. L., and Dollar, P · 2014
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2014
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Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
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ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L · 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
Bolukbasi, T., Chang, K.-W., Zou, J., Saligrama, V., and Kalai, A · 2016
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Fair prediction with disparate impact: A study of bias in recidivism prediction instrument
Chouldechova, A · 2016
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False positives, false negatives, and false analyses: A rejoinder to ”machine bias: There’s software used across the country to predict future criminals. and it’s biased against blacks.”
Flores, A. W., Bechtel, K., and Lowenkamp, C. T · 2016
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The case for process fairness in learning: Feature selection for fair decision making
Grgic-Hlaca, N., Zafar, M. B., Gummadi, K. P., and Weller, A · 2016
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Equality of opportunity in supervised learning
Hardt, M., Price, E., and Srebro, N · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Semantic and context-aware linguistic model for bias detection
Kuang, S. and Davison, B. D · 2016
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Bias amplification and bias unmasking
Middleton, J. A., Scott, M. A., Diakow, R., and Hill, J. L · 2016
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Employed persons by detailed occupation, sex, race, and hispanic or latino ethnicity
of Labor Statistics, U. B · 2016
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Should instrumental variables be used as matching variables?
Wooldridge, J. M · 2016
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Fairness in criminal justice risk assessments: The state of the art
Berk, R., Heidari, H., Jabbari, S., Kearns, M., and Roth, A · 2017
Cited alongside, same era.
Semantics derived automatically from language corpora contain human-like biases
Caliskan, A., Bryson, J. J., and Narayanan, A · 2017
Cited alongside, same era.
English adjectives
Hugsy · 2017
Cited alongside, same era.
Counterfactual fairness
Kusner, M. J., Loftus, J. R., Russell, C., and Silva, R · 2017
Cited alongside, same era.
Gender as a variable in natural-language processing: Ethical considerations
Larson, B. N · 2017
Cited alongside, same era.
Men also like shopping: Reducing gender bias amplification using corpus-level constraints
Zhao, J., Wang, T., Yatskar, M., Ordonez, V., and Chang, K.-W · 2017
Cited alongside, same era.
A survey on bias and fairness in machine learning
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., and Galstyan, A · 2019
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Social data: Biases, methodological pitfalls, and ethical boundaries
Olteanu, A., Castillo, C., Diaz, F., and Kiciman, E · 2019
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Reducing gender bias in word-level language models with a gender-equalizing loss function
Qian, Y., Muaz, U., Zhang, B., and Hyun, J. W · 2019
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A framework for understanding unintended consequences of machine learning
Suresh, H. and Guttag, J. V · 2019
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Balanced datasets are not enough: Estimating and mitigating gender bias in deep image representations
Wang, T., Zhao, J., Yatskar, M., Chang, K.-W., and Ordonez, V · 2019
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Interventions over predictions: Reframing the ethical debate for actuarial risk assessment
Barabas, C., Dinakar, K., Ito, J., Virza, M., and Zittrain, J · 2018
Cited alongside, same era.
Gender shades: Intersectional accuracy disparities in commercial gender classification
Buolamwini, J. and Gebru, T · 2018
Cited alongside, same era.
An intersectional definition of fairness
Foulds, J., Islam, R., Keya, K. N., and Pan, S · 2018
Cited alongside, same era.
Multicalibration: Calibration for the (computationally-identifiable) masses
Hebert-Johnson, U., Kim, M. P., Reingold, O., and Rothblum, G. N · 2018
Cited alongside, same era.
Women also snowboard: Overcoming bias in captioning models
Hendricks, L. A., Burns, K., Saenko, K., Darrell, T., and Rohrbach, A · 2018
Cited alongside, same era.
The misgendering machines: Trans/HCI implications of automatic gender recognition
Keyes, O · 2018
Cited alongside, same era.
Unlocking fairness: a trade-off revisited
Wick, M., Panda, S., and Tristan, J.-B · 2019
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Towards causal VQA: Revealing and reducing spurious correlations by invariant and covariant semantic editing
Agarwal, V., Shetty, R., and Fritz, M · 2020
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Language (technology) is power: A critical survey of “bias” in nlp
Blodgett, S. L., Barocas, S., III, H. D., and Wallach, H · 2020
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Towards threshold invariant fair classification
Chen, M. and Wu, M · 2020
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Fair generative modeling via weak supervision
Choi, K., Grover, A., Singh, T., Shu, R., and Ermon, S · 2020
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Underspecification presents challenges for credibility in modern machine learning
D’Amour, A., Heller, K., Moldovan, D., Adlam, B., Alipanahi, B., Beutel, A., Chen, C., Deaton, J., Eisenstein, J., Hoffman, M. D., Hormozdiari, F., Houlsby, N., Hou, S., Jerfel, G., Karthikesalingam, A., Lucic, M., Ma, Y., McLean, C., Mincu, D., Mitani, A., Montanari, A., Nado, Z., Natarajan, V., Nielson, C., Osborne, T. F., Raman, R., Ramasamy, K., Sayres, R., Schrouff, J., Seneviratne, M., Sequeira, S., Suresh, H., Veitch, V., Vladymyrov, M., Wang, X., Webster, K., Yadlowsky, S., Yun, T., Zhai, X., and Sculley, D · 2020
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The false promise of risk assessments: Epistemic reform and the limits of fairness
Green, B · 2020
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Robustness in machine learning explanations: Does it matter?
Hancox-Li, L · 2020
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Jain, N., Olmo, A., Sengupta, S., Manikonda, L., and Kambhampati, S · 2020
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Mitigating gender bias amplification in distribution by posterior regularization
Jia, S., Meng, T., Zhao, J., and Chang, K.-W · 2020
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Towards debiasing sentence representations
Liang, P. P., Li, I. M., Zheng, E., Lim, Y. C., Salakhutdinov, R., and Morency, L.-P · 2020
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Predictive multiplicity in classification
Marx, C. T., du Pin Calmon, F., and Ustun, B · 2020
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On counterfactual explanations under predictive multiplicity
Pawelczyk, M., Broelemann, K., and Kasneci, G · 2020
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How we’ve taught algorithms to see identity: Constructing race and gender in image databases for facial analysis
Scheuerman, M. K., Wade, K., Lustig, C., and Brubaker, J. R · 2020
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Don’t judge an object by its context: Learning to overcome contextual bias
Singh, K. K., Mahajan, D., Grauman, K., Lee, Y. J., Feiszli, M., and Ghadiyaram, D · 2020
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Mitigating gender bias in captioning systems
Tang, R., Du, M., Li, Y., Liu, Z., and Hu, X · 2020
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“it’s complicated”: Negotiating accessibility and (mis)representation in image descriptions of race, gender, and disability
Bennett, C. L., Gleason, C., Scheuerman, M. K., Bigham, J. P., Guo, A., and To, A · 2021
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