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 …

Currently, there is little agreement as to how Natural Language Generation (NLG) systems
should be evaluated, with a particularly high degree of variation in the way that human …

The hidden costs of artificial intelligence, from natural resources and labor to privacy and
freedom What happens when artificial intelligence saturates political life and depletes the …

Human evaluation is critical for validating the performance of text-to-image generative
models, as this highly cognitive process requires deep comprehension of text and images …

Current advances in research, development and application of artificial intelligence (AI)
systems have yielded a far-reaching discourse on AI ethics. In consequence, a number of …

The AI Now Institute at New York University is an interdisciplinary research institute
dedicated to understanding the social implications of AI technologies. It is the first university …

Captions that describe or explain charts help improve recall and comprehension of the
depicted data and provide a more accessible medium for people with visual disabilities …

Crowdsourcing markets provide workers with a centralized place to find paid work. What
may not be obvious at first glance is that, in addition to the work they do for pay, crowd …

Data is central to the development and evaluation of machine learning (ML) models.
However, the use of problematic or inappropriate datasets can result in harms when the …

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 …