Toward General Design Principles for Generative AI Applications

TL;DR

This paper proposes seven design principles for creating safe, effective, and user-friendly generative AI applications, based on recent research in human-AI co-creation and variability.

Contribution

It introduces a set of seven principles specifically tailored for designing generative AI applications, emphasizing handling variability, user control, and safety considerations.

Findings

Seven design principles grounded in human-AI co-creation research.

Guidelines for managing generative variability and potential harms.

Recommendations for designing safe and effective AI applications.

Abstract

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.