Incorporating machine learning (ML) components into software products raises new
software-engineering challenges and exacerbates existing ones. Many researchers have …

Current approaches to building general-purpose AI systems tend to produce systems with
both beneficial and harmful capabilities. Further progress in AI development could lead to …

Increased delegation of commercial, scientific, governmental, and personal activities to AI
agents—systems capable of pursuing complex goals with limited supervision—may …

As Artificial Intelligence (AI) continues to advance rapidly, it becomes increasingly important
to consider AI's ethical and societal implications. In this paper, we present a bottom-up …

Recent advances in the field of deep learning and impressive performance of deep neural
networks (DNNs) for perception have resulted in an increased demand for their use in …

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

Safety and responsibility evaluations of advanced AI models are a critical but developing
field of research and practice. In the development of Google DeepMind's advanced AI …

With the growing need to regulate AI systems across a wide variety of application domains, a
new set of occupations has emerged in the industry. The so-called responsible Artificial …

Though ML practitioners increasingly employ various Responsible ML (RML) strategies,
their methodological approach in practice is still unclear. In particular, the constraints …

This paper introduces a collaborative, human-centered taxonomy of AI, algorithmic and
automation harms. We argue that existing taxonomies, while valuable, can be narrow …