Forecasting has always been at the forefront of decision making and planning. The
uncertainty that surrounds the future is both exciting and challenging, with individuals and …

It is well understood from literature that the performance of a machine learning (ML) model is
upper bounded by the quality of the data. While researchers and practitioners have focused …

AI models are increasingly applied in high-stakes domains like health and conservation.
Data quality carries an elevated significance in high-stakes AI due to its heightened …

As AI systems demonstrate increasingly strong predictive performance, their adoption has
grown in numerous domains. However, in high-stakes domains such as criminal justice and …

A surge of interest in explainable AI (XAI) has led to a vast collection of algorithmic work on
the topic. While many recognize the necessity to incorporate explainability features in AI …

Machine learning (ML) models are now routinely deployed in domains ranging from criminal
justice to healthcare. With this newfound ubiquity, ML has moved beyond academia and …

Research in machine learning (ML) has argued that models trained on incomplete or biased
datasets can lead to discriminatory outputs. In this commentary, we propose moving the …

The rapid advancement of artificial intelligence (AI) is changing our lives in many ways. One
application domain is data science. New techniques in automating the creation of AI, known …

Today, the prominence of data science within organizations has given rise to teams of data
science workers collaborating on extracting insights from data, as opposed to individual data …

Machine learning (ML) depends on data to train and verify models. Very often, organizations
outsource processes related to data work (ie, generating and annotating data and …