In this work, we survey a breadth of literature that has revealed the limitations of
predominant practices for dataset collection and use in the field of machine learning. We …

Artificial intelligence (AI) has emerged as a fundamental component of global agricultural
research that is poised to impact on many aspects of plant science. In digital phenomics, AI …

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 …

In response to growing concerns of bias, discrimination, and unfairness perpetuated by
algorithmic systems, the datasets used to train and evaluate machine learning models have …

Various tools and practices have been developed to support practitioners in identifying,
assessing, and mitigating fairness-related harms caused by AI systems. However, prior …

Research in machine learning (ML) has argued that models trained on incomplete or biased
datasets can lead to discriminatory outputs. In this commentary, we propose moving the …

Despite the growing consensus that stakeholders affected by AI systems should participate
in their design, enormous variation and implicit disagreements exist among current …

Human annotated data plays a crucial role in machine learning (ML) research and
development. However, the ethical considerations around the processes and decisions that …

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

Recent years have seen the development of many open-source ML fairness toolkits aimed
at helping ML practitioners assess and address unfairness in their systems. However, there …