Evaluating the Social Impact of Generative AI Systems in Systems and
  Society
Authors: name: Irene Solaiman, name: Zeerak Talat, name: William Agnew, name: Lama Ahmad, name: Dylan Baker, name: Su Lin Blodgett, name: Hal Daumé III, name: Jesse Dodge, name: Ellie Evans, name: Sara Hooker, name: Yacine Jernite, name: Alexandra Sasha Luccioni, name: Alberto Lusoli, name: Margaret Mitchell, name: Jessica Newman, name: Marie-Therese Png, name: Andrew Strait, name: Apostol Vassilev

Generative AI systems across modalities, ranging from text (including code),
image, audio, and video, have broad social impacts, but there is no official
standard for means of evaluating those impacts or for which impacts should be
evaluated. In this paper, we present a guide that moves toward a standard
approach in evaluating a base generative AI system for any modality in two
overarching categories: what can be evaluated in a base system independent of
context and what can be evaluated in a societal context. Importantly, this
refers to base systems that have no predetermined application or deployment
context, including a model itself, as well as system components, such as
training data. Our framework for a base system defines seven categories of
social impact: bias, stereotypes, and representational harms; cultural values
and sensitive content; disparate performance; privacy and data protection;
financial costs; environmental costs; and data and content moderation labor
costs. Suggested methods for evaluation apply to listed generative modalities
and analyses of the limitations of existing evaluations serve as a starting
point for necessary investment in future evaluations. We offer five overarching
categories for what can be evaluated in a broader societal context, each with
its own subcategories: trustworthiness and autonomy; inequality,
marginalization, and violence; concentration of authority; labor and
creativity; and ecosystem and environment. Each subcategory includes
recommendations for mitigating harm.