In recent years, machine learning has transitioned from a field of academic research interest
to a field capable of solving real-world business problems. However, the deployment of …

The rapid development of Artificial Intelligence (AI) technology has enabled the deployment
of various systems based on it. However, many current AI systems are found vulnerable to …

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 …

Whose labels should a machine learning (ML) algorithm learn to emulate? For ML tasks
ranging from online comment toxicity to misinformation detection to medical diagnosis …

Datasets that power machine learning are often used, shared, and reused with little visibility
into the processes of deliberation that led to their creation. As artificial intelligence systems …

AI-based systems are software systems with functionalities enabled by at least one AI
component (eg, for image-, speech-recognition, and autonomous driving). AI-based systems …

Responsible Artificial Intelligence (RAI) is widely considered as one of the greatest scientific
challenges of our time and is key to increase the adoption of Artificial Intelligence (AI) …

Machine learning classifiers for human-facing tasks such as comment toxicity and
misinformation often score highly on metrics such as ROC AUC but are received poorly in …

Many subfields of machine learning share a common stumbling block: evaluation. Advances
in machine learning often evaporate under closer scrutiny or turn out to be less widely …

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 …