Organizations cannot address demographic disparities that they cannot see. Recent
research on machine learning and fairness has emphasized that awareness of sensitive …

As calls for fair and unbiased algorithmic systems increase, so too does the number of
individuals working on algorithmic fairness in industry. However, these practitioners often do …

Decisions based on algorithmic, machine learning models can be unfair, reproducing biases
in historical data used to train them. While computational techniques are emerging to …

An emerging concern in algorithmic fairness is the tension with privacy interests. Data
minimization can restrict access to protected attributes, such as race and ethnicity, for bias …

Existing regulations often prohibit model developers from accessing protected attributes
(gender, race, etc.) during training. This leads to scenarios where fairness assessments …

We address two emerging concerns in algorithmic fairness:(i) redundant encodings of race–
the notion that machine learning models encode race with probability nearing one as the …

As more industries integrate machine learning into socially sensitive decision processes like
hiring, loan-approval, and parole-granting, we are at risk of perpetuating historical and …

Machine learning models are prone to biased decisions due to biases in the datasets they
are trained on. In this paper, we introduce a novel data augmentation technique to create a …

Assessing the fairness of a decision making system with respect to a protected class, such
as gender or race, is challenging when class membership labels are unavailable …

Abstract “Big Data” and data-mined inferences are affecting more and more of our lives, and
concerns about their possible discriminatory effects are growing. Methods for discrimination …