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问题。 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其用户进行猜测，这两者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，这可能会造成心理伤害并影响人们对系统的访问;收集的数据也可能以不必要的方式使用，例如未经人们同意组装的大型面部识别数据库。 即使系统设计者的意图是好的，这些伤害也经常发生，因为当前的设计实践几乎没有提供关于如何推理可能的伤害的具体指导。 该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强调了潜在的隐私问题和通常会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，将为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明智选择的机会。所提出的具体方法被称为设计隐私（PtD），这是一种新颖的研究方法，旨在帮助面向消费者的人工智能技术的创造者：（i）对利益相关者之间的最终用户之间的严重的、用例特定的隐私问题如何与拟议的人工智能概念的预期效用或价值进行权衡进行建模;以及（ii）了解如何以尊重利益相关者的隐私问题的方式（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。 首先是开发一种算法隐私入侵的分类法，以操作消费者AI所带来的独特隐私伤害，并将这些伤害映射到AI系统的独特能力和要求上。第二个是使用迭代方法开发PtD，该方法将工业界和学术界的专家和从业人员纳入其中。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AI技术方面的比较。 两个关键的预期成果是一个设计案例库，其中出现并解决了隐私问题，以及一本指导手册，其中包含指导和建议，以帮助消费者人工智能技术的创造者在设计过程中集中考虑隐私问题。该奖项反映了NSF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.