This systematic literature review synthesizes the conceptualizations of ethical principles in AI
auditing literature and the knowledge contributions to the stakeholders of AI auditing. We …

Mitigating bias in algorithmic systems is a critical issue drawing attention across
communities within the information and computer sciences. Given the complexity of the …

AbstractScale the model, scale the data, scale the compute'is the reigning sentiment in the
world of generative AI today. While the impact of model scaling has been extensively …

Discriminative self-supervised learning allows training models on any random group of
internet images, and possibly recover salient information that helps differentiate between the …

On the one hand, complexity science and enactive and embodied cognitive science
approaches emphasize that people, as complex adaptive systems, are ambiguous …

In this paper, we examine computational approaches for measuring the" fairness" of image
tagging systems, finding that they cluster into five distinct categories, each with its own …

Previous work has largely considered the fairness of image captioning systems through the
underspecified lens of “bias.” In contrast, we present a set of techniques for measuring five …

One of the most concrete measures towards meaningful AI accountability is to
consequentially assess and report the systems' performance and impact. However, the …

Contemporary artificial intelligence and algorithmic processes address deep-seated
existential challenges and modes of desire. In so doing, they produce computational …

Text data can pose a risk of harm. However, the risks are not fully understood, and how to
handle, present, and discuss harmful text in a safe way remains an unresolved issue in the …