设计生成式人工智能用户反馈的未来
Designing the Future of User Feedback for Generative AI
- International Computer Science Institute（国际计算机科学研究所）
- University of California, Berkeley（加州大学伯克利分校）
- eBay Inc.（eBay公司）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究通过与eBay合作，针对生成式AI用户反馈机制进行基准评估，发现可发现性差、术语不清等问题，并据此提出最佳实践建议，设计并测试了高效灵活的原型反馈工具，以提升用户参与和产品改进。
AI中文摘要：
部署后来自用户的反馈可以成为监测和改进生成式人工智能系统及功能的一种成本效益高、可扩展且具有代表性的手段。当有效实施时，提供此类反馈可以增加用户对生成式人工智能系统的参与度和信任度。政府法规和行业指南呼吁进行部署后的用户参与，但关于设计可供消费者使用且为产品团队提供可操作输入的机制，指导却很少。我们开展了一项多阶段研究，这是学术研究人员与eBay之间的合作。我们对当前行业方法的基准评估发现了常见问题，包括缺乏可发现性、术语不清晰以及对用户价值的忽视。基于这些发现，我们制定了最佳实践建议，并设计并测试了一个反馈收集工具的原型。该工具旨在为用户提供高效、灵活且积极的反馈体验，并为产品团队提供关于性能和潜在问题的丰富数据，且格式可用。
英文摘要：
Post-deployment feedback from users can be a cost-effective, scalable, and representative means to monitor and improve generative AI systems and features. When implemented effectively, giving such feedback can increase users' engagement with and trust in GenAI systems. Government regulations and industry guidelines call for post-deployment user engagement, but there is little guidance on designing mechanisms that are usable for consumers and provide actionable input for product teams. We conducted a multi-phase study as a collaboration between academic researchers and eBay. Our benchmark evaluation of current industry approaches identified common issues including lack of discoverability, unclear terminology, and inattention to user value. Based on these findings, we developed best-practice recommendations and designed and tested a prototype feedback-collection tool. The tool aimed to provide users with an efficient, flexible, and positive feedback-giving experience, and provide product teams with rich data on performance and potential problems in a usable format.
