One of the main concerns about fairness in machine learning (ML) is that, in order to achieve
it, one may have to trade off some accuracy. To overcome this issue, Hardt et al. proposed …

Fairness-aware learning aims at constructing classifiers that not only make accurate
predictions, but also do not discriminate against specific groups. It is a fast-growing area of …

Fairness is becoming a rising concern wrt machine learning model performance. Especially
for sensitive fields such as criminal justice and loan decision, eliminating the prediction …

Differentially-private mechanisms for text generation typically add carefully calibrated noise
to input words and use the nearest neighbor to the noised input as the output word. When …

Building machine learning models that are fair with respect to an unprivileged group is a
topical problem. Modern fairness-aware algorithms often ignore causal effects and enforce …

As machine learning algorithms grow in popularity and diversify to many industries, ethical
and legal concerns regarding their fairness have become increasingly relevant. We explore …

Protecting privacy in contemporary NLP models is gaining in importance. So does the need
to mitigate social biases of such models. But can we have both at the same time? Existing …

Given the increasing importance of machine learning (ML) in our lives, several algorithmic
fairness techniques have been proposed to mitigate biases in the outcomes of the ML …

As the data-driven decision process becomes dominating for industrial applications, fairness-
aware machine learning arouses great attention in various areas. This work proposes …

Dataset bias is one of the prevailing causes of unfairness in machine learning. Addressing
fairness at the data collection and dataset preparation stages therefore becomes an …