The Generative AI Ethics Playbook
Jessie J. Smith, Wesley Hanwen Deng, William H. Smith, Maarten Sap, Nicole DeCario, Jesse Dodge
TL;DR
The Generative AI Ethics Playbook articulates a lifecycle-based framework for identifying and mitigating harms in generative AI, NLP, and computer vision systems. It organizes guidance into six stages from problem formulation to deployment, coupling transparency checklists with a harm taxonomy (Representational, Allocative, Quality of Service, Interpersonal, Societal) and concrete mitigation strategies. The work collates best practices, case studies, and tools (e.g., datasheets, impact assessments, red-teaming, and participatory approaches) to support ethical decision-making, documentation, and accountability throughout development and use. By promoting early ethics integration, rigorous evaluation, and post-deployment safeguards, the playbook aims to reduce social and environmental harms while enhancing transparency, reproducibility, and responsible innovation in generative AI systems.
Abstract
The Generative AI Ethics Playbook provides guidance for identifying and mitigating risks of machine learning systems across various domains, including natural language processing, computer vision, and generative AI. This playbook aims to assist practitioners in diagnosing potential harms that may arise during the design, development, and deployment of datasets and models. It offers concrete strategies and resources for mitigating these risks, to help minimize negative impacts on users and society. Drawing on current best practices in both research and ethical considerations, this playbook aims to serve as a comprehensive resource for AI/ML practitioners. The intended audience of this playbook includes machine learning researchers, engineers, and practitioners who are involved in the creation and implementation of generative and multimodal models (e.g., text-to-text, image-to-image, text-to-image, text-to-video). Specifically, we provide transparency/documentation checklists, topics of interest, common questions, examples of harms through case studies, and resources and strategies to mitigate harms throughout the Generative AI lifecycle. This playbook was made collaboratively over the course of 16 months through extensive literature review of over 100 resources and peer-reviewed articles, as well as through an initial group brainstorming session with 18 interdisciplinary AI ethics experts from industry and academia, and with additional feedback from 8 experts (5 of whom were in the initial brainstorming session). We note that while this playbook provides examples, discussion, and harm mitigation strategies, research in this area is ongoing. Our playbook aims to be a practically useful survey, taking a high-level view rather than aiming for covering the entire existing body of research.