Recent advances in AI combine large language models (LLMs) with vision encoders that
bring forward unprecedented technical capabilities to leverage for a wide range of …

To make explainable artificial intelligence (XAI) systems trustworthy, understanding harmful
effects is important. In this paper, we address an important yet unarticulated type of negative …

In response to rising concerns surrounding the safety, security, and trustworthiness of
Generative AI (GenAI) models, practitioners and regulators alike have pointed to AI red …

Audits are critical mechanisms for identifying the risks and limitations of deployed artificial
intelligence (AI) systems. However, the effective execution of AI audits remains incredibly …

Datasets are foundational to many breakthroughs in modern artificial intelligence. Many
recent achievements in the space of natural language processing (NLP) can be attributed to …

Large language models (LLMs) are increasingly capable of providing users with advice in a
wide range of professional domains, including legal advice. However, relying on LLMs for …

Responsible AI (RAI) guidelines aim to ensure that AI systems respect democratic values.
While a step in the right direction, they currently fail to impact practice. Our work discusses …

Fairness metrics have become a useful tool to measure how fair or unfair a machine
learning system may be for its stakeholders. In the context of recommender systems …

Prompt-based interfaces for Large Language Models (LLMs) have made prototyping and
building AI-powered applications easier than ever before. However, identifying potential …

What counts as legitimate AI ethics labor, and consequently, what are the epistemic terms on
which AI ethics claims are rendered legitimate? Based on 75 interviews with technologists …