@article{yurochkin_training_2019,
	title = {Training individually fair {ML} models with {Sensitive} {Subspace} {Robustness}},
	doi = {10.48550/arxiv.1907.00020},
	abstract = {We consider training machine learning models that are fair in the sense that
their performance is invariant under certain sensitive perturbations to the
inputs. For example, the performance of a resume screening system should be
invariant under changes to the gender and/or ethnicity of the applicant. We
formalize this notion of algorithmic fairness as a variant of individual
fairness and develop a distributionally robust optimization approach to enforce
it during training. We also demonstrate the effectiveness of the approach on
two ML tasks that are susceptible to gender and racial biases.},
	author = {Yurochkin, Mikhail and Bower, Amanda and Sun, Yuekai},
	year = {2019},
}

{"_id":"wQckHL9XZ9WTcFSzn","bibbaseid":"yurochkin-bower-sun-trainingindividuallyfairmlmodelswithsensitivesubspacerobustness-2019","author_short":["Yurochkin, M.","Bower, A.","Sun, Y."],"bibdata":{"bibtype":"article","type":"article","title":"Training individually fair ML models with Sensitive Subspace Robustness","doi":"10.48550/arxiv.1907.00020","abstract":"We consider training machine learning models that are fair in the sense that their performance is invariant under certain sensitive perturbations to the inputs. For example, the performance of a resume screening system should be invariant under changes to the gender and/or ethnicity of the applicant. We formalize this notion of algorithmic fairness as a variant of individual fairness and develop a distributionally robust optimization approach to enforce it during training. We also demonstrate the effectiveness of the approach on two ML tasks that are susceptible to gender and racial biases.","author":[{"propositions":[],"lastnames":["Yurochkin"],"firstnames":["Mikhail"],"suffixes":[]},{"propositions":[],"lastnames":["Bower"],"firstnames":["Amanda"],"suffixes":[]},{"propositions":[],"lastnames":["Sun"],"firstnames":["Yuekai"],"suffixes":[]}],"year":"2019","bibtex":"@article{yurochkin_training_2019,\n\ttitle = {Training individually fair {ML} models with {Sensitive} {Subspace} {Robustness}},\n\tdoi = {10.48550/arxiv.1907.00020},\n\tabstract = {We consider training machine learning models that are fair in the sense that\ntheir performance is invariant under certain sensitive perturbations to the\ninputs. For example, the performance of a resume screening system should be\ninvariant under changes to the gender and/or ethnicity of the applicant. We\nformalize this notion of algorithmic fairness as a variant of individual\nfairness and develop a distributionally robust optimization approach to enforce\nit during training. We also demonstrate the effectiveness of the approach on\ntwo ML tasks that are susceptible to gender and racial biases.},\n\tauthor = {Yurochkin, Mikhail and Bower, Amanda and Sun, Yuekai},\n\tyear = {2019},\n}\n\n","author_short":["Yurochkin, M.","Bower, A.","Sun, Y."],"key":"yurochkin_training_2019","id":"yurochkin_training_2019","bibbaseid":"yurochkin-bower-sun-trainingindividuallyfairmlmodelswithsensitivesubspacerobustness-2019","role":"author","urls":{},"metadata":{"authorlinks":{}},"html":""},"bibtype":"article","biburl":"https://bibbase.org/f/Ceciz2iNjTZgQNtDc/mypubs_mar_2024.bib","dataSources":["m8Y57GfgnRrMKZTQS","epk5yKhDyD37NAsSC"],"keywords":[],"search_terms":["training","individually","fair","models","sensitive","subspace","robustness","yurochkin","bower","sun"],"title":"Training individually fair ML models with Sensitive Subspace Robustness","year":2019}