Artificial intelligence (AI) technologies have become the key driver of innovation in society.
However, numerous vulnerabilities of AI systems can lead to negative consequences for …

Generative AI systems across modalities, ranging from text, image, audio, and video, have
broad social impacts, but there exists no official standard for means of evaluating those …

The widespread adoption of large language models (LLMs) makes it important to recognize
their strengths and limitations. We argue that in order to develop a holistic understanding of …

Technology companies continue to invest in efforts to incorporate responsibility in their
Artificial Intelligence (AI) advancements, while efforts to audit and regulate AI systems …

This work identifies 18 foundational challenges in assuring the alignment and safety of large
language models (LLMs). These challenges are organized into three different categories …

An emerging body of research indicates that ineffective cross-functional collaboration–the
interdisciplinary work done by industry practitioners across roles–represents a major barrier …

In the context of passive acoustic monitoring (PAM) better models are needed to reliably
gain insights from large amounts of raw, unlabeled data. Bioacoustics foundation models …

We formalize predictive optimization, a category of decision-making algorithms that use
machine learning (ML) to predict future outcomes of interest about individuals. For example …

Forming a reliable judgement of a machine learning (ML) model's appropriateness for an
application ecosystem is critical for its responsible use, and requires considering a broad …

AI progress is creating a growing range of risks and opportunities, but it is often unclear how
they should be navigated. In many cases, the barriers and uncertainties faced are at least …