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: 2133169
• 负责人: Ferdinando Fioretto
• 金额: $ 26.5万
• 依托单位: Syracuse University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2023-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2133169&HistoricalAwards=false
• 关键词: 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，人口普查数据被用来分配资金，并向各州和各司法管辖区分配关键资源。由此产生的决定可能对参与的个人产生重大的社会和经济影响。在许多情况下，发布的数据包含敏感信息，其隐私受到严格的监管，差异隐私（DP）已成为保护数据隐私的首选范例。然而，尽管差异隐私在发布的数据上提供了强有力的隐私保证，但最近很明显，它可能会在下游决策过程中引发偏见和公平问题，包括联邦资金的分配，国会席位的分配以及疫苗和治疗药物的分配。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，而且人们对此知之甚少。该项目解决了隐私，公平，偏见和决策过程的交叉点的知识差距。它将为差异隐私工具提供新的视角，以在关键决策过程中共同解决公平和隐私问题。它将量化这些应用中产生的不同影响，并提出新的机制和缓解技术来克服其中一些问题。这些贡献将被嵌入到建模和软件工具中，以使该技术广泛可用和适用。从科学的角度来看，该项目将开发新一代隐私保护工具，通过利用差异隐私，优化和编程语言的知识，将解决其设计中的偏见和公平问题，而不是事后的想法。该项目沿着五个方向贡献了新的科学知识：（1）它识别和理解下游决策过程的结构，这些决策过程在使用DP数据发布时可能会受到公平问题的影响;（2）它识别和理解可能引入偏见的DP机制的结构;（3）它定义了理论框架来描述和推理偏见和公平问题;（4）设计缓解措施，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）开发建模和软件工具，以自动识别和解释偏见和公平问题，该奖项反映了NSF的法定使命，并被认为值得支持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。

项目成果

Fairness Increases Adversarial Vulnerability

• DOI: 10.48550/arxiv.2211.11835
• 发表时间: 2022-11
• 期刊: ArXiv
• 作者: Cuong Tran;Keyu Zhu;Ferdinando Fioretto;P. V. Hentenryck

Post-processing of Differentially Private Data: A Fairness Perspective

差分隐私数据的后处理：公平的角度

• DOI: 10.24963/ijcai.2022/559
• 发表时间: 2022
• 期刊: International Joint Conference on Artificial Intelligence (IJCAI
• 作者: Zhu, Keyu;Fioretto, Ferdinando;Van Hentenryck, Pascal
• 通讯作者: Van Hentenryck, Pascal

End-to-End Learning for Fair Ranking Systems

公平排名系统的端到端学习

• DOI: 10.1145/3485447.3512247
• 发表时间: 2022
• 期刊: WWW '22: Proceedings of the ACM Web Conference 2022
• 作者: Kotary, James;Fioretto, Ferdinando;Van Hentenryck, Pascal;Zhu, Ziwei
• 通讯作者: Zhu, Ziwei

FairDP: Certified Fairness with Differential Privacy

• DOI: 10.48550/arxiv.2305.16474
• 发表时间: 2023-05
• 期刊: ArXiv
• 作者: K. Tran;Ferdinando Fioretto;Issa Khalil;M. Thai;Nhathai Phan

Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey

• DOI: 10.24963/ijcai.2022/766
• 发表时间: 2022-02
• 期刊: ArXiv
• 作者: Ferdinando Fioretto;Cuong Tran;P. V. Hentenryck;Keyu Zhu