Privacy for All: Ensuring Fair and Equitable Privacy Protections

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Fair decision making using privacy-protected data (2019, neutral): “w can be viewed as a generalization of differential privacy. Both of these focus on settings distinct from ours. Conceptually closest to our work is the recent position paper in which Ekstrand et al. [12] raise a number of questions about the equitable provision of privacy protections (privacy-fairn”
Stable and Fair Classification (2019, methodology): “, 2014) have expanded the application of methods to achieve both goals; see a recent paper (Ekstrand et al., 2018) for more discussions.”
On the Compatibility of Privacy and Fairness (2019, background): “[8] raised questions of whether statistical notions of equitable predictive power, such as equalized odds [10], are compatible with privacy.”
The Ethics of AI Ethics: An Evaluation of Guidelines (2019, methodology): “1 3 many privacy-friendly techniques for the use of data sets and learning algorithms have been developed, using methods where AI systems’ “sight” is “darkened” via cryptography, differential or stochastic privacy (Ekstrand et al. 2018; Baron and Musolesi 2017; Duchi et al. 2013; Singla et al. 2014)”