Collaborative Research: SaTC: CORE: Medium: Privacy Through Design: A Design Methodology to Promote the Creation of Privacy-Conscious Consumer AI

该项目在开发直接影响消费者的人工智能(AI)技术时集中考虑隐私。使用人工智能可以帮助系统适应特定人的目标和能力；然而，人工智能系统通常需要收集数据并对用户进行猜测，这两种情况都可能是侵入性的，并造成伤害。例如，人工智能系统有时会对个人敏感特征做出错误的推断，这既会造成心理伤害，也会影响人们对系统的访问；收集的数据也可能被用于不必要的方式，例如在未经人们同意的情况下组装大型面部识别数据库。这些危害经常发生，即使系统设计人员是善意的，因为当前的设计实践几乎没有提供关于如何推理可能的危害的具体指导。这个项目将通过创建设计方法和指南来解决这个问题，这些方法和指南突出了潜在的隐私问题和经常增加这些风险的设计选择。学生和行业研究人员参与方法的开发和评估将给这项工作带来直接的教育影响，并增加未来基于人工智能的系统在隐私和安全风险方面做出知情选择的机会。提出的具体方法被称为通过设计的隐私(PTD)，这是一种新的研究方法，旨在帮助面向消费者的人工智能技术的创建者：(I)建立利益相关者中最终用户对用例特定隐私的关注如何与拟议的人工智能概念的预期效用或价值进行权衡的模型；以及，(Ii)了解如何以尊重利益相关者隐私关切的方式(重新)设计这些概念，同时保留设计的预期效用。做这项工作有三个主要的科学贡献。首先是制定算法隐私入侵的分类，以操作消费者人工智能带来的独特隐私损害，并将这些损害映射到人工智能系统的独特能力和要求上。第二个目标是使用迭代方法开发PTD，其中包括工业界和学术界的专家和从业者。第三是正式评估通过PTD开发的产品与通过设计消费者人工智能技术的现有行业标准开发的产品相比如何。两个关键的设想输出是出现并解决隐私问题的设计案例存储库，以及一本指南，其中包含工作表和建议，以帮助消费者人工智能技术的创建者在其设计过程中集中考虑隐私。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This project centers consideration of privacy in the development of artificial intelligence (AI) technologies that directly impact consumers. Using AI can help systems adapt to specific people's goals and abilities; however, AI systems typically require collecting data and making guesses about their users, both of which can be intrusive and cause harms. For instance, AI systems sometimes make wrong inferences about personal, sensitive characteristics that can cause both psychological harm and affect people's access to systems; the data collected can also be used in unwanted ways, such as large facial recognition databases assembled without people's consent. These harms often happen, even when system designers are well-intentioned, because current design practice provides little specific guidance on how to reason about possible harms. This project will tackle this problem by creating design methods and guidelines that highlight potential privacy issues and design choices that often increase these risks. Student and industry researcher involvement in the development and evaluation of the methods will give the work both direct educational impact and increase the chance that future AI-based systems will make informed choices around privacy and safety risks.The specific method proposed is called Privacy through Design (PtD), a novel research methodology to help creators of consumer-facing AI technologies: (i) model how acute, use-case specific privacy concerns among end-users among stakeholders trade off against the envisioned utility or value of proposed AI concepts; and, (ii) understand how to (re-)design those concepts in a manner that respects stakeholders' privacy concerns of while retaining the envisioned utility of the design. Doing this work makes three main scientific contributions. The first is to develop a taxonomy of algorithmic privacy intrusions to operationalize the unique privacy harms entailed by consumer AI and map those harms onto the unique capabilities and requirements of AI systems. This second is to develop PtD using an iterative methodology incorporating experts and practitioners in industry and academia. The third is to formally evaluate how products developed through PtD compare to those developed through existing industry standards for designing consumer AI technologies. Two key envisioned outputs are a repository of design cases in which privacy concerns emerge and are resolved, and a guidebook with worksheets and recommendations to help creators of consumer AI technologies center consideration of privacy in their design processes.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.