Mitigating bias in algorithmic systems is a critical issue drawing attention across
communities within the information and computer sciences. Given the complexity of the …

Real-world machine learning applications need to be thoroughly evaluated to meet critical
product requirements for model release, to ensure fairness for different groups or …

As speech processing moves toward more data-hungry models, data selection and
acquisition become crucial to building better systems. Recent efforts have championed …

The automatic prediction of court case judgments using Deep Learning and Natural
Language Processing is challenged by the variety of norms and regulations, the inherent …

The evaluation of spoken language understanding (SLU) systems is often restricted to
assessing their global performance or examining predefined subgroups of interest …

Understanding peculiar and anomalous behavior of machine learning models for specific
data subgroups is a fundamental building block of model performance and fairness …

End-to-End Spoken Language Understanding models are generally evaluated according to
their overall accuracy, or separately on (a priori defined) data subgroups of interest. We …

An entire ecosystem of methodologies and tools revolves around scientific workflow
management. They cover crucial non-functional requirements that standard workflow …

In this paper, we explore the trade-off between fairness and accuracy when data is biased
and unbiased. We introduce two versions of a modified loss function: Group Equity and …

Data slice finding is an emerging technique for validating machine learning (ML) models by
identifying and analyzing subgroups in a dataset that exhibit poor performance, often …