Data-driven algorithms are only as good as the data they work with, while datasets,
especially social data, often fail to represent minorities adequately. Representation Bias in …

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 …

The predominant de facto paradigm of testing ML models relies on either using only held-out
data to compute aggregate evaluation metrics or by assessing the performance on different …

Existing model evaluation tools mainly focus on evaluating classification models, leaving a
gap in evaluating more complex models, such as object detection. In this paper, we develop …

We are being constantly judged by automated decision systems that have been widely
criticised for being discriminatory and unfair. Since an algorithm is only as good as the data …

Warning: This paper contains examples of language that some people may find offensive.
Transformer-based Natural Language Processing models have become the standard for …

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

Data-driven decision-making shapes every corner of human life today, from autonomous
vehicles to healthcare, and even in predictive policing and criminal sentencing. A critical …

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 …