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Attacks from adversarial machine learning (ML) have the potential to be used "for good": they can be used to run counter to the existing power structures within ML, creating breathing space for those who would otherwise be the targets of surveillance and control.
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Obfuscation: A User’s Guide for Privacy and Protest
Brunton, F. and Nissenbaum, H. F · 2015
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Shokri, R., Stronati, M., Song, C., and Shmatikov, V · 2017
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Buolamwini, J. and Gebru, T · 2018
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Doringer, B. and Felderer, B · 2018
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Elinas, P., Bonilla, E. V., and Tiao, L. C · 2020
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European Commission · 2020
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AttriGuard: A Practical Defense Against Attribute Inference Attacks via Adversarial Machine Learning
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POTs: Protective Optimization Technologies
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Race for Profit: How Banks and the Real Estate Industry Undermined Black Homeownership
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NeurIPS · 2020
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To Live in Their Utopia: Why Algorithmic Systems Create Absurd Outcomes
Alkhatib, A · 2021
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