Crowdsourcing—the process of using the internet to outsource research participation to
“workers”—has considerable benefits, enabling research to be conducted quickly, efficiently …

Human annotated data plays a crucial role in machine learning (ML) research and
development. However, the ethical considerations around the processes and decisions that …

With the widespread availability and pervasiveness of artificial intelligence (AI) in many
application areas across the globe, the role of crowdsourcing has seen an upsurge in terms …

Recent research has demonstrated that cognitive biases such as the confirmation bias or the
anchoring effect can negatively affect the quality of crowdsourced data. In practice, however …

The importance of meeting data quality standards in the context of Machine Learning (ML)
development pipelines is explored in this study. It delves deep into why good data is crucial …

Algorithm aversion occurs when humans are reluctant to use algorithms despite their
superior performance. Studies show that giving users outcome control by providing agency …

The fitness of the systems in which Machine Learning (ML) is used depends greatly on good-
quality data. Specifications on what makes a good-quality dataset have traditionally been …

Every day, we have to decide multiple times, whether and how much personal data we allow
to be collected. This decision is not trivial, since there are many legitimate and important …

The rapid entry of machine learning approaches in our daily activities and high-stakes
domains demands transparency and scrutiny of their fairness and reliability. To help gauge …

Recent studies indicated GPT-4 outperforms online crowd workers in data labeling
accuracy, notably workers from Amazon Mechanical Turk (MTurk). However, these studies …