Toward General Design Principles for Generative AI Applications
This work addresses the problem of designing human-AI applications for researchers and practitioners, but it is incremental as it synthesizes existing research into principles.
The paper tackles the need for design guidance in generative AI applications to ensure productive and safe use, resulting in a set of seven principles grounded in generative variability, including six focused on AI characteristics and one on mitigating harms.
Generative AI technologies are growing in power, utility, and use. As generative technologies are being incorporated into mainstream applications, there is a need for guidance on how to design those applications to foster productive and safe use. Based on recent research on human-AI co-creation within the HCI and AI communities, we present a set of seven principles for the design of generative AI applications. These principles are grounded in an environment of generative variability. Six principles are focused on designing for characteristics of generative AI: multiple outcomes & imperfection; exploration & control; and mental models & explanations. In addition, we urge designers to design against potential harms that may be caused by a generative model's hazardous output, misuse, or potential for human displacement. We anticipate these principles to usefully inform design decisions made in the creation of novel human-AI applications, and we invite the community to apply, revise, and extend these principles to their own work.