text: 42,121,1006096760744370176,20309837,Michael Veale,"‘Debiasing’/FATML methods assume ML modellers hold sensitive data (eg ethnicity, sexuality). Privacy problem. Our new #ICML2018 paper uses secure multiparty computation to train ‘fair’ models without seeing these, and allows regulators to verify decisions. <LINK> <LINK> (it's also important to remember throughout this that 'debiasing' approaches only are appropriate in very narrow situations, and are no silver bullet for socio-technical harms and concerns that may involve machine learning) this might be of interest to @zacharylipton @realjoshkroll @tforcworc @realhamed @Miles_Brundage @mort___ @ruggieris",https://arxiv.org/abs/1806.03281,"Recent work has explored how to train machine learning models which do not discriminate against any subgroup of the population as determined by sensitive attributes such as gender or race. To avoid disparate treatment, sensitive attributes should not be considered. On the other hand, in order to avoid disparate impact, sensitive attributes must be examined, e.g., in order to learn a fair model, or to check if a given model is fair. We introduce methods from secure multi-party computation which allow us to avoid both. By encrypting sensitive attributes, we show how an outcome-based fair model may be learned, checked, or have its outputs verified and held to account, without users revealing their sensitive attributes. ",Blind Justice: Fairness with Encrypted Sensitive Attributes,3,"['‘Debiasing’/FATML methods assume ML modellers hold sensitive data (eg ethnicity, sexuality). Privacy problem. Our new #ICML2018 paper uses secure multiparty computation to train ‘fair’ models without seeing these, and allows regulators to verify decisions. <LINK> <LINK>', ""(it's also important to remember throughout this that 'debiasing' approaches only are appropriate in very narrow situations, and are no silver bullet for  socio-technical harms and concerns that may involve machine learning)"", 'this might be of interest to @zacharylipton @realjoshkroll @tforcworc @realhamed @Miles_Brundage @mort___ @ruggieris']",18,06,611