Despite impressive performance in many benchmark datasets, AI models can still make
mistakes, especially among out-of-distribution examples. It remains an open question how …

Recent years have seen the development of many open-source ML fairness toolkits aimed
at helping ML practitioners assess and address unfairness in their systems. However, there …

Nowadays, machine learning projects have become more and more relevant to various real-
world use cases. The success of complex Neural Network models depends upon many …

The development of AI applications is a multidisciplinary effort, involving multiple roles
collaborating with the AI developers, an umbrella term we use to include data scientists and …

Data science (DS) projects often follow a lifecycle that consists of laborious tasks for data
scientists and domain experts (eg, data exploration, model training, etc.). Only till recently …

Data scientists often have to use other presentation tools (eg, Microsoft PowerPoint) to
create slides to communicate their analysis obtained using computational notebooks. Much …

Computational notebooks allow data scientists to express their ideas through a combination
of code and documentation. However, data scientists often pay attention only to the code …

Creating presentation slides is a critical but time-consuming task for data scientists. While
researchers have proposed many AI techniques to lift data scientists' burden on data …

Data storytelling plays an important role in data workers' daily jobs since it boosts team
collaboration and public communication. However, to make an appealing data story, data …

What makes large language models (LLMs) impressive is also what makes them hard to
evaluate: their diversity of uses. To evaluate these models, we must understand the …