Powerful machine learning models can automatize decisions in critical areas of human lives, such as criminal pre-trial detention and hiring. These models are often trained on large datasets of historical decisions. However, past discriminatory human behavior may have tainted these decisions and datasets with discimination. Therefore, it is imperative to ask how can we ensure … Continue reading "How to train models that do not propagate discrimination?"
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Face recognition technology, the ability for computers to identify people from photos or videos of their faces, has become increasingly controversial in the last few years. The software is rapidly becoming more common in applications from police work and surveillance to smart phone access, entertainment applications, and even medical diagnosis. There are those who wish … Continue reading "Regulating Face Recognition Technology: An Introduction"
Read MoreFull Abstract: Recent studies found that using machine learning for social applications can lead to injustice in the form of racist, sexist, and otherwise unfair and discriminatory outcomes. To address this challenge, recent machine learning algorithms have been designed to limit the likelihood such unfair behavior occurs. However, these approaches typically assume the data used … Continue reading "Paper: Fairness Guarantees under Demographic Shift"
Read MoreFull Abstract: The goal of this paper is to develop a practical and general-purpose approach to construct confidence intervals for differentially private parametric estimation. We find that the parametric bootstrap is a simple and effective solution. It cleanly reasons about variability of both the data sample and the randomized privacy mechanism and applies “out of … Continue reading "Paper: Parametric Bootstrap for Differentially Private Confidence Intervals"
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