Collaborative Research: SaTC: CORE: Small: Privacy and Fairness in Critical Decision Making

协作研究：SaTC：核心：小型：关键决策中的隐私和公平

• 批准号: 2345483
• 负责人: Ferdinando Fioretto
• 金额: $ 26.5万
• 依托单位: University of Virginia Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2345483&HistoricalAwards=false
• 关键词: Collaborative; Research; SaTC; CORE; Small; Collaborative; Research; SaTC; CORE; Small

Many agencies or companies release statistics about groups of individuals that are then used as input to critical decision processes. For example, census data is used to allocate funds and distribute critical resources to states and jurisdictions. The resulting decisions can have significant societal and economic impacts for participating individuals. In many cases, the released data contain sensitive information whose privacy is strictly regulated and Differential Privacy (DP) has become the paradigm of choice for protecting data privacy. However, while differential privacy provides strong privacy guarantees on the released data, it has become apparent recently that it may induce biases and fairness issues in downstream decision processes, including the allotment of federal funds, apportionment of congressional seats, and distribution of vaccines and therapeutics. These biases and fairness issues may adversely affect the health, well-being, and sense of belonging of many individuals, and are poorly understood. This project addresses this knowledge gap at the intersection of privacy, fairness, bias, and decision processes. It will offer novel perspectives on differential privacy tools to address fairness and privacy jointly in critical decision processes. It will quantify the disparate impact arising in these applications and contribute novel mechanisms and mitigation techniques to overcome some of these issues. These contributions will be embedded in modeling and software tools to make the technology widely available and applicable.From a scientific standpoint, this project will develop a new generation of privacy-preserving tools that, by exploiting knowledge from differential privacy, optimization, and programming languages, will address biases and fairness issues in their designs, not as an afterthought. The project contributes new scientific knowledge along with five directions: (1) it identifies and understands the structure of downstream decision processes that may be subject to fairness issues when using DP data releases; (2) it identifies and understands the structure of DP mechanisms that may introduce biases; (3) it defines theoretical frameworks to characterize and reason about biases and fairness issues; (4) it designs mitigation measures that would remove or alleviate the biases and fairness issues, finding an appropriate tradeoff between privacy, accuracy, and fairness; (5) it develops modeling and software tools to automatically identify and explain biases and fairness issues, and derive mitigation measures from the specification of the decision process.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.

许多机构或公司发布了有关个人群体的统计数据，然后将其用作关键决策过程的输入。例如，人口普查数据用于分配资金并将关键资源分配给各州和司法管辖区。由此产生的决定可能会对参与个人产生重大的社会和经济影响。在许多情况下，已发布的数据包含严格监管隐私和差异隐私（DP）的敏感信息已成为保护数据隐私的首选范式。但是，尽管差异隐私在发布的数据上提供了强大的隐私保证，但最近显然可以在下游决策过程中引起偏见和公平问题，包括分配联邦资金，国会席位的分配以及疫苗和治疗学的分配。这些偏见和公平问题可能会对许多人的健康，福祉和归属感产生不利影响，并且理解不足。该项目在隐私，公平，偏见和决策过程的交汇处解决了这一知识差距。它将提供有关差异隐私工具的新观点，以在关键决策过程中共同解决公平和隐私。它将量化这些应用中产生的不同影响，并贡献新的机制和缓解技术来克服其中一些问题。这些贡献将嵌入建模和软件工具中，以使技术可广泛可用和适用。从科学的角度来看，该项目将开发新一代的隐私保护工具，通过利用差异隐私，优化和编程语言知识，将解决其设计中的偏见和公平问题，而不是在其后进行的偏见和公平问题。该项目贡献了新的科学知识以及五个方向：（1）它识别并了解下游决策过程的结构，这些决策过程可能会在使用DP数据发布时可能会受到公平问题的影响； （2）它识别并了解可能引入偏见的DP机制的结构； （3）它定义了理论框架来表征和理由偏见和公平问题； （4）它设计了缓解措施，以消除或减轻偏见和公平问题，在隐私，准确性和公平性之间找到适当的权衡； （5）它开发了建模和软件工具，以自动识别和解释偏见和公平问题，并从决策过程的规范中得出缓解措施。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响审查标准来评估通过评估的支持。