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Advances in algorithmic fairness have largely omitted sexual orientation and gender identity.
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Toward automating HIV identification: Machine learning for rapid identification of HIV-related social media data
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Cyberbullying and LGBTQ youth: A systematic literature review and recommendations for prevention and intervention
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Cited alongside, same era.
Fairness in machine learning: Lessons from political philosophy. In Conference on Fairness, Accountability and Transparency . 149–159
Reuben Binns. 2018 · 2018
Cited alongside, same era.
A comparative study of logistic regression based machine learning techniques for prediction of early virological suppression in antiretroviral initiating HIV patients
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Gender shades: Intersectional accuracy disparities in commercial gender classification. In Conference on Fairness, Accountability and Transparency . 77–91
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Cited alongside, same era.
Not all laws are created equal: Legal differences in state non-discrimination laws and the impact of LGBT employment protections
Queer in AI 2019 Workshop
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Troll Patrol
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LGBTI Rights
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McKane Andrus, Elena Spitzer, Jeffrey Brown, and Alice Xiang. 2020 · 2020
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Explainability for fair machine learning
Tom Begley, Tobias Schwedes, Christopher Frye, and Ilya Feige. 2020 · 2020
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122 Shades of Gray
Joel Bellenson. 2019 · 2020
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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Ian Burn. 2018 · 2018
Cited alongside, same era.
A case study of algorithm-assisted decision making in child maltreatment hotline screening decisions. In Conference on Fairness, Accountability and Transparency . 134–148
Alexandra Chouldechova, Diana Benavides-Prado, Oleksandr Fialko, and Rhema Vaithianathan. 2018 · 2018
Cited alongside, same era.
The measure and mismeasure of fairness: A critical review of fair machine learning
Sam Corbett-Davies and Sharad Goel. 2018 · 2018
Cited alongside, same era.
Gaydar and the fallacy of objective measurement
Andrew Gelman, G Marrson, and Daniel Simpson. 2018 · 2018
Cited alongside, same era.
Maya Gupta, Andrew Cotter, Mahdi Milani Fard, and Serena Wang. 2018 · 2018
Cited alongside, same era.
Gender recognition or gender reductionism? The social implications of embedded gender recognition systems. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems . 1–13
Foad Hamidi, Morgan Klaus Scheuerman, and Stacy M Branham. 2018 · 2018
Cited alongside, same era.
Preventing disparate treatment in sequential decision making. In International Joint Conference on Artificial Intelligence . 2248–2254
Hoda Heidari and Andreas Krause. 2018 · 2018
Cited alongside, same era.
‘You look fine!’ Ableist experiences by people with invisible disabilities
Shanna K Kattari, Miranda Olzman, and Michele D Hanna. 2018 · 2018
Cited alongside, same era.
Awareness in practice: Tensions in access to sensitive attribute data for antidiscrimination. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency . 492–500
Miranda Bogen, Aaron Rieke, and Shazeda Ahmed. 2020 · 2020
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The Oxford Handbook of Global LGBT and Sexual Diversity Politics
Michael J Bosia, Sandra M McEvoy, and Momin Rahman. 2020 · 2020
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We’re not all in this together: On COVID-19, intersectionality, and structural inequality
Lisa Bowleg. 2020 · 2020
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Gender: A few definitions
Brook. n.d. · 2020
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Language models are few-shot learners
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Contrastive fairness in machine learning
Tapabrata Chakraborti, Arijit Patra, and J Alison Noble. 2020 · 2020
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On the privacy risks of algorithmic fairness
Hongyan Chang and Reza Shokri. 2020 · 2020
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Exchanging lessons between algorithmic fairness and domain generalization
Elliot Creager, Jörn-Henrik Jacobsen, and Richard Zemel. 2020 · 2020
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Bullied and blackmailed: Gay men in Morocco falling victims to outing campaign sparked by Instagram model
Tom Embury-Dennis. 2020 · 2020
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Is your classifier actually biased? Measuring fairness under uncertainty with Bernstein bounds
Kawin Ethayarajh. 2020 · 2020
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What personal data is considered sensitive?
European Commission. n.d. · 2020
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Cyberbullying detection with fairness constraints
Oguzhan Gencoglu. 2020 · 2020
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Drag queens and artificial intelligence: Should computers decide what is ‘toxic’ on the internet?
Alessandra Gomes, Dennys Antonialli, and Thiago Dias Oliva. 2019 · 2020
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Adversarial learning for counterfactual fairness
Vincent Grari, Sylvain Lamprier, and Marcin Detyniecki. 2020 · 2020
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Towards a critical race methodology in algorithmic fairness. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency . 501–512
Alex Hanna, Emily Denton, Andrew Smart, and Jamila Smith-Loud. 2020 · 2020
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Aligning AI with shared human values
Dan Hendrycks, Collin Burns, Steven Basart, Andrew Critch, Jerry Li, Dawn Song, and Jacob Steinhardt. 2020a · 2020
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Measuring massive multitask language understanding
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US: LGBT people face healthcare barriers
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Can I trust my fairness metric? Assessing fairness with unlabeled data and Bayesian inference
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Fairness without demographics through adversarially reweighted learning
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Joseph McCormick. 2015 · 2020
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Ethical limitations of algorithmic fairness solutions in health care machine learning
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Cohort-derived machine learning models for individual prediction of chronic kidney disease in people living with Human Immunodeficiency Virus: A prospective multicenter cohort study
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Closest in time.