Toward General Design Principles for Generative AI Applications 130-144

Justin D. Weisz, Michael J. Muller, Jessica He, Stephanie Houde
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引用次数: 24

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.
生成式人工智能应用的一般设计原则130-144
生成式人工智能技术在功率、效用和使用方面都在增长。随着生成技术被纳入主流应用,有必要指导如何设计这些应用以促进生产和安全使用。基于最近在HCI和AI社区中对人类-AI共同创造的研究,我们提出了一套生成式AI应用程序设计的七项原则。这些原则是建立在生成可变性的环境基础上的。针对生成式人工智能的特点设计了六个原则:多结果和不完善性;exploration&control;心理模型和解释。此外,我们敦促设计师设计防止生成模型的危险输出、误用或潜在的人类流离失所可能造成的潜在危害。我们期望这些原则在创造新的人类-人工智能应用程序时有效地为设计决策提供信息,我们邀请社区将这些原则应用、修改和扩展到他们自己的工作中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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