DASS: Exploring how convergence methods foster shared accountability to reveal, map, and mitigate the sources and dynamics of bias across social service provisioning systems

DASS：探索融合方法如何促进共同责任，以揭示、映射和减轻社会服务提供系统中偏见的来源和动态

• 批准号: 2217706
• 负责人: Margaret Hinrichs
• 金额: $ 74.88万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2217706&HistoricalAwards=false
• 关键词: DASS; Exploring; how; convergence; methods

Social services, traditionally, have been organized around their missions, such as education or safety or health. A newer approach, called "wrap-around services" or "systems of care," organizes services around individuals and their specific context and needs. These systems face many challenges when applied in real-world settings. Application processes often focus more on the potential of technologies and less on the realities, histories, and needs of communities. The proposed research addresses this gap by evaluating the implementation of a system of care in a real-world setting. The research involves studying how civic participation may be better supported and bias reduced in the development and integration of systems of care for communities. A key outcome of this proposal is to understand the influence of processes inspired by justice, equity, diversity, and inclusion to intentionally check bias and reduce disparities in the design and application of public service provisioning software systems. The goal of the proposed work is to advance accountable software systems through developing generalizable and localizable practices for exploring how to identify specific and systemic sources of bias, improve public service provisioning outcomes, and minimize disparities from biased program outcomes.The proposed work builds from existing theory and the project team’s experience in designing and executing software systems that support services to the public. The intent is to identify and map the different types of threats to accountability that can be anticipated within the socio-computational ecosystem of care services. The team is studying dynamics of how bias might propagate across the system through regulations, social contexts, data, and lack of representation in the software development process, among other factors. Once such dynamics are mapped, the team will explore how to attend to bias through a series of interventions planned and conducted in collaboration with community members. These will include mapping the system collaboratively, and holding knowledge convergence workshops, algorithmic impact assessments, and using computational analytic techniques that can augment governance opportunities in the design and deployment of these systems of care. An outcome measure to be tracked through this process is the effectiveness of a system at responding to individual-level threats and creating bias immunity at the systems level through shared stewardship and accountability. The type of products expected from this research are advances in theory (articulation of novel threats, new understandings of dynamics & treatments), specific products related to an algorithmic accountability policy toolkit, and guidelines for practice customizable to local use cases through generalizable convergence workshops.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.

传统上，社会服务是围绕其使命组织的，如教育、安全或健康。一种较新的方法称为“环绕式服务”或“护理系统”，它围绕个人及其具体情况和需要组织服务。这些系统在实际应用中面临许多挑战。申请过程往往更多地关注技术的潜力，而较少关注社区的现实、历史和需求。拟议的研究通过评估在现实世界环境中实施护理系统来解决这一差距。这项研究涉及研究如何在发展和整合社区护理系统方面更好地支持公民参与和减少偏见。该提案的一个关键成果是了解受公正、公平、多样性和包容性启发的过程的影响，以有意检查偏见并减少公共服务提供软件系统的设计和应用中的差异。拟议工作的目标是通过开发可推广和本地化的做法，探索如何确定具体的和系统性的偏差来源，提高公共服务的提供成果，并尽量减少偏差的程序outcomes.The拟议工作建立在现有的理论和项目团队的经验，在设计和执行软件系统，支持服务公众的问责软件系统。其目的是确定和映射不同类型的威胁，以问责制，可以预期在社会计算生态系统的护理服务。该团队正在研究偏见如何通过法规，社会背景，数据以及软件开发过程中缺乏代表性等因素在整个系统中传播的动态。一旦这种动态被绘制出来，该团队将探索如何通过与社区成员合作计划和进行的一系列干预措施来解决偏见问题。这些将包括协同绘制系统，举办知识融合研讨会，算法影响评估，以及使用计算分析技术，这些技术可以在设计和部署这些护理系统时增加治理机会。通过这一过程跟踪的一个成果衡量标准是，系统在应对个人层面的威胁和通过共同管理和问责在系统层面建立偏见免疫方面的效力。该研究的成果包括理论上的进步（新威胁的阐述、对动力学处理的新理解）、与算法问责政策工具包相关的具体产品，以及通过可推广的融合研讨会针对当地用例定制的实践指南。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。

项目成果

Two-sided Cultural Niches: Topic Overlap, Geospatial Correlation, and Local Group Activities on Event-based Social Networks

• DOI: 10.1145/3593743.3593758
• 发表时间: 2023-05
• 期刊: Proceedings of the 11th International Conference on Communities and Technologies
• 作者: Julia Hsin-Ping Hsu;H. Shin;Noseong Park;Myeong Lee