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

该项目以对消费者产生直接影响的人工智能（AI）技术开发过程中的隐私问题为中心。使用人工智能可以帮助系统适应特定的人的目标和能力；然而，人工智能系统通常需要收集数据并对其用户进行猜测，这两种做法都可能具有侵入性并造成危害。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，这可能会造成心理伤害，并影响人们对系统的访问；收集到的数据也可能被用于不必要的方式，比如未经用户同意而建立的大型面部识别数据库。这些危害经常发生，即使当系统设计者是善意的，因为目前的设计实践提供了很少具体的指导，如何推理可能的危害。这个项目将通过创建设计方法和指导方针来解决这个问题，这些方法和指导方针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这些方法的开发和评估，将给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明智选择的机会。提出的具体方法被称为“通过设计保护隐私”（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造者：(i)建模最终用户和利益相关者之间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；并且，（ii）了解如何（重新）设计这些概念，以尊重利益相关者的隐私问题，同时保留设计的预期效用。这项工作有三个主要的科学贡献。首先是开发一种算法隐私入侵的分类法，以实施消费者人工智能带来的独特隐私危害，并将这些危害映射到人工智能系统的独特功能和需求上。第二个是使用结合工业界和学术界专家和实践者的迭代方法开发PtD。第三个是正式评估通过PtD开发的产品与通过现有行业标准开发的产品在设计消费者人工智能技术方面的对比。设想的两个关键产出是一个设计案例库，其中出现了隐私问题并得到了解决，以及一本包含工作表和建议的指南，以帮助消费者人工智能技术的创造者在设计过程中考虑隐私。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.