Large, diverse datasets hold tremendous promise, if only we can derive statistical insights from them. But often, these datasets are siloed and withheld, because of privacy concerns. Differential privacy can mitigate these concerns — it provides a strong, attractive privacy guarantee that protects data owners from risks associated disclosure of Read more…
Important decisions such as police deployment, loan approvals, hiring, and parole from incarceration are beginning to be made by machine learning algorithms. This has lent urgency to the question of whether these algorithms are fair. A recent well-publicized analysis of recidivism risk predictions studied NorthPointe’s proprietary COMPAS algorithm used in Read more…
Classical tools for rigorously analyzing data make the assumption that the analysis is static: the models to be fit, and the hypotheses to be tested are fixed independently of the data, and preliminary analysis of the data does not feed back into the data gathering procedure. On the other hand, Read more…