SaTC: CORE: Large: Collaborative: Accountable Information Use: Privacy and Fairness in Decision-Making Systems

SaTC：核心：大型：协作：负责任的信息使用：决策系统中的隐私和公平

基本信息

批准号：
1704527
负责人：
Helen Nissenbaum
金额：
$ 90万
依托单位：
Cornell University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-06-01 至 2024-05-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1704527&HistoricalAwards=false
关键词：
SaTC CORE Large Collaborative Accountable

项目摘要

Increasingly, decisions and actions affecting people's lives are determined by automated systems processing personal data. Excitement about these systems has been accompanied by serious concerns about their opacity and the threats that they pose to privacy, fairness, and other values. Recognizing these concerns, the investigators seek to make real-world automated decision-making systems accountable for privacy and fairness by enabling them to detect and explain violations of these values. The technical work is informed by, and applied to, online advertising, healthcare, and criminal justice, in collaboration with and as advised by domain experts. Addressing privacy and fairness in decision systems requires providing formal definitional frameworks and practical system designs. The investigators provide new notions of privacy and fairness that deal with both protected information itself and proxies for it, while handling context-dependent, normative definitions of violations. A fundamental tension they address pits the access given to auditors of a system against the system owners' intellectual property protections and the confidentiality of the personal data used by the system. The investigators decompose such auditing into stages, where the level of access granted to an auditor is increased when potential (but not explainable) violations of privacy or fairness are detected. Workshops and public releases of code and data amplify the investigators' interactions with policy makers and other stakeholders. Their partnerships with outreach organizations encourage diversity.

越来越多地，影响人们生活的决策和行动取决于处理个人数据的自动化系统。对这些系统的兴奋是通过对它们的不透明度以及对隐私，公平和其他价值观构成的威胁的严重关注来实现的。认识到这些问题，调查人员试图通过使他们能够检测和解释违反这些价值的行为来使真实世界的自动决策系统对隐私和公平负责。技术工作由与域专家的合作和建议，并由在线广告，医疗保健和刑事司法申请并应用于在线广告，医疗保健和刑事司法。解决决策系统中的隐私和公平性需要提供正式的定义框架和实用的系统设计。调查人员提供了有关隐私和公平性的新笔记，这些说明既涉及受保护的信息本身和代理，同时处理上下文依赖于违规的正常定义。他们针对系统所有者的知识产权保护以及系统使用的个人数据的信心，将其解决的基本张力解决了访问系统的访问。调查人员将此类审核分解为阶段，当检测到违反隐私或公平性的潜力（但未解释）侵犯审计师的访问水平。代码和数据的研讨会和公众发布扩大了调查人员与决策者和其他利益相关者的互动。他们与外展组织的合作伙伴关系鼓励多样性。

项目成果

期刊论文数量（15）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Characterizing Twitter Users Who Engage in Adversarial Interactions against Political Candidates

DOI：
10.1145/3313831.3376548
发表时间：
2020-04
期刊：
Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
影响因子：
0
作者：
Yiqing Hua;Mor Naaman;Thomas Ristenpart
通讯作者：
Yiqing Hua;Mor Naaman;Thomas Ristenpart

Contextual Integrity Up and Down the Data Food Chain

数据食物链上下游的上下文完整性

DOI：
发表时间：
2019
期刊：
Theoretical inquiries in law
影响因子：
0
作者：
Nissenbaum, Helen
通讯作者：
Nissenbaum, Helen

VACCINE: Using Contextual Integrity For Data Leakage Detection

疫苗：使用上下文完整性进行数据泄漏检测

DOI：
10.1145/3308558.3313655
发表时间：
2019
期刊：
Proceedings of the 2019 World Wide Web Conference
影响因子：
0
作者：
Shvartzshnaider, Yan;Pavlinovic, Zvonimir;Balashankar, Ananth;Wies, Thomas;Subramanian, Lakshminarayanan;Nissenbaum, Helen;Mittal, Prateek
通讯作者：
Mittal, Prateek

Computer Vision and Conflicting Values: Describing People with Automated Alt Text.

计算机视觉和冲突的价值观：用自动替代文本描述人。