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The power of machine learning systems not only promises great technical progress, but risks societal harm.
Characterizations of an empirical influence function for detecting influential cases in regression
Cook, R. and Weisberg, S · 1980
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Measuring individual differences in implicit cognition: The implicit association test
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The new york times annotated corpus, 2008
Sandhaus, E · 2008
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Fairness through awareness
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., and Zemel, R · 2012
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Explaining robot actions
Lomas, M., Chevalier, R., II, E. C., Garrett, R., Hoare, J., and Kopack, M · 2012
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Linguistic regularities in continuous space word representations
Mikolov, T., t. Yih, W., and Zweig, G · 2013
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Discrimination in online ad delivery
Sweeney, L · 2013
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Glove: Global vectors for word representation
Pennington, J., Socher, R., and Manning, C · 2014
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Improving Distributional Similarity with Lessons Learned from Word Embeddings
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Machine bias: There’s software used across the country to predict future criminals. and it’s biased against blacks
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Equality of opportunity in supervised learning
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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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Understanding Black-box Predictions via Influence Functions
Koh, P. W. and Liang, P · 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
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Evaluating the stability of embedding-based word similarities
Antoniak, M. and Mimno, D · 2018
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Simplewiki:database download, 2018
Wikimedia · 2018
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Kleinberg, J., Mullainathan, S., and Raghavan, M · 2016
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Why should i trust you?: Explaining the predictions of any classifier
Ribeiro, M. T., Singh, S., and Guestrin, C · 2016
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Efficient estimation of word representations in vector space
Mikolov, T., Chen, K., Corrado, G., and Dean, J
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Gender bias in coreference resolution: Evaluation and debiasing methods
Zhao, J., Wang, T., Yatskar, M., Ordonez, V., and Chang, K.-W · 2018
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