Artificial intelligence (AI) and algorithmic decision making are having a profound impact on
our daily lives. These systems are vastly used in different high-stakes applications like …

Deep learning (DL) models have achieved impressive performance in various domains such
as medicine, finance, and autonomous vehicle systems with advances in computing power …

AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …

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 …

The growth of the Internet has expanded the amount of data expressed by users across
multiple platforms. The availability of these different worldviews and individuals' emotions …

The development and deployment of machine learning systems can be executed easily with
modern tools, but the process is typically rushed and means-to-an-end. Lack of diligence …

Efforts to make machine learning more widely accessible have led to a rapid increase in
Auto-ML tools that aim to automate the process of training and deploying machine learning …

Data is central to the development and evaluation of machine learning (ML) models.
However, the use of problematic or inappropriate datasets can result in harms when the …

Organizations rely on machine learning engineers (MLEs) to operationalize ML, ie, deploy
and maintain ML pipelines in production. The process of operationalizing ML, or MLOps …

The confusion matrix, a ubiquitous visualization for helping people evaluate machine
learning models, is a tabular layout that compares predicted class labels against actual …