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The task of image captioning implicitly involves gender identification.
Conditional random fields: Probabilistic models for segmenting and labeling sequence data
Lafferty, J., McCallum, A., and Pereira, F. C. (2001) · 2001
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Seizing opportunities
Data, D., Sugar, I., Mutual, O., and Cell, M. (2004) · 2004
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Exposure to benevolent sexism and complementary gender stereotypes: consequences for specific and diffuse forms of system justification
Jost, J. T. and Kay, A. C. (2005) · 2005
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The handbook of language and gender
Holmes, J. and Meyerhoff, M. (2008) · 2008
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Learning with annotation noise
Beigman, E. and Klebanov, B. B. (2009) · 2009
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Building classifiers with independency constraints
Calders, T., Kamiran, F., and Pechenizkiy, M. (2009) · 2009
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Classifying without discriminating
Kamiran, F. and Calders, T. (2009) · 2009
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Women and news: A long and winding road
Ross, K. and Carter, C. (2011) · 2011
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Unbiased look at dataset bias
Torralba, A., Efros, A. A., et al. (2011) · 2011
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Fairness through awareness
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., and Zemel, R. (2012) · 2012
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Reporting bias and knowledge extraction
Gordon, J. and Van Durme, B. (2013) · 2013
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Discrimination in online ad delivery
Sweeney, L. (2013) · 2013
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Mechanisms of linguistic bias: How words reflect and maintain stereotypic expectancies
Beukeboom, C. J. et al. (2014) · 2014
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Age and gender estimation of unfiltered faces
Eidinger, E., Enbar, R., and Hassner, T. (2014) · 2014
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Microsoft coco captions: Data collection and evaluation server
Chen, X., Fang, H., Lin, T.-Y., Vedantam, R., Gupta, S., Dollár, P., and Zitnick, C. L. (2015) · 2015
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Certifying and removing disparate impact
Feldman, M., Friedler, S. A., Moeller, J., Scheidegger, C., and Venkatasubramanian, S. (2015) · 2015
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Deep visual-semantic alignments for generating image descriptions
Karpathy, A. and Fei-Fei, L. (2015) · 2015
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Age and gender classification using convolutional neural networks
Levi, G. and Hassner, T. (2015) · 2015
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It’s a man’s wikipedia? assessing gender inequality in an online encyclopedia
Wagner, C., Garcia, D., Jadidi, M., and Strohmaier, M. (2015) · 2015
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Analyzing the behavior of visual question answering models
Agrawal, A., Batra, D., and Parikh, D. (2016) · 2016
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Minimalistic cnn-based ensemble model for gender prediction from face images
Antipov, G., Berrani, S.-A., and Dugelay, J.-L. (2016) · 2016
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Big data’s disparate impact
Barocas, S. and Selbst, A. D. (2016) · 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. Y., Saligrama, V., and Kalai, A. T. (2016) · 2016
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Recycling privileged learning and distribution matching for fairness
Quadrianto, N. and Sharmanska, V. (2017) · 2017
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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) · 2017
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Turning a blind eye: Explicit removal of biases and variation from deep neural network embeddings
Alvi, M., Zisserman, A., and Nellåker, C. (2018) · 2018
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Convolutional image captioning
Aneja, J., Deshpande, A., and Schwing, A. G. (2018) · 2018
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How gender and skin tone modifiers affect emoji semantics in twitter
Barbieri, F. and Camacho-Collados, J. (2018) · 2018
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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) · 2016
Cited alongside, same era.
Equality of opportunity in supervised learning
Hardt, M., Price, E., Srebro, N., et al. (2016) · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J. (2016) · 2016
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Gender classification by deep learning on millions of weakly labelled images
Jia, S., Lansdall-Welfare, T., and Cristianini, N. (2016) · 2016
Cited alongside, same era.
Local deep neural networks for gender recognition
Mansanet, J., Albiol, A., and Paredes, R. (2016) · 2016
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Seeing through the human reporting bias: Visual classifiers from noisy human-centric labels
Misra, I., Lawrence Zitnick, C., Mitchell, M., and Girshick, R. (2016) · 2016
Cited alongside, same era.
Stereotyping and bias in the flickr30k dataset
van Miltenburg, E. (2016) · 2016
Cited alongside, same era.
Buolamwini, J. and Gebru, T. (2018) · 2018
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Women also snowboard: Overcoming bias in captioning models
Burns, K., Hendricks, L. A., Darrell, T., and Rohrbach, A. (2018) · 2018
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Adversarial removal of demographic attributes from text data
Elazar, Y. and Goldberg, Y. (2018) · 2018
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A multi-agent system for the classification of gender and age from images
González-Briones, A., Villarrubia, G., De Paz, J. F., and Corchado, J. M. (2018) · 2018
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Right for the right reason: Training agnostic networks
Jia, S., Lansdall-Welfare, T., and Cristianini, N. (2018) · 2018
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Explicit bias discovery in visual question answering models
Manjunatha, V., Saini, N., and Davis, L. S. (2018) · 2018
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Social cues, social biases: stereotypes in annotations on people images
Otterbacher, J. (2018) · 2018
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Convnets and imagenet beyond accuracy: Understanding mistakes and uncovering biases
Stock, P. and Cisse, M. (2018) · 2018
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Adversarial removal of gender from deep image representations
Wang, T., Zhao, J., Chang, K.-W., Yatskar, M., and Ordonez, V. (2018) · 2018
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Swag: A large-scale adversarial dataset for grounded commonsense inference
Zellers, R., Bisk, Y., Schwartz, R., and Choi, Y. (2018) · 2018
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Mitigating unwanted biases with adversarial learning
Zhang, B. H., Lemoine, B., and Mitchell, M. (2018) · 2018
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Hyperface: A deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition
Ranjan, R., Patel, V. M., and Chellappa, R. (2019) · 2019
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