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来组织的，例如教育、安全或健康。一种称为“全方位服务”或“护理系统”的新方法是围绕个人及其特定背景和需求来组织服务。系统在现实环境中应用时面临许多挑战。应用程序通常更多地关注技术的潜力，而不是现实、历史和社区的需求。拟议的研究通过评估护理系统的实施来解决这一差距。该研究涉及研究公民如何在现实世界中。在社区护理系统的开发和整合过程中，可以更好地支持参与并减少偏见。该提案的一个关键成果是了解受正义、公平、多样性和包容性启发的流程的影响，以有意识地检查偏见并减少差异。拟议工作的目标是通过开发可普遍化和可本地化的实践来推进负责任的软件系统，以探索如何识别具体和系统性的偏见来源，改善公共服务提供结果，并最大限度地减少偏见。来自有偏见的计划结果的差异。拟议的工作以现有理论和项目团队在设计和执行支持公众服务的软件系统方面的经验为基础，其目的是识别和映射在护理社会计算生态系统中可以预见的不同类型的问责威胁。该团队正在研究偏见如何通过法规、社会背景、数据和软件开发过程中缺乏代表性等因素在系统中传播的动态。一旦绘制出这些动态，该团队将探索如何参与其中。通过一系列有计划的干预措施来消除偏见这些将包括协作绘制系统、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来增强这些护理系统的设计和部署的治理机会。通过这个过程跟踪的是系统在响应个人层面的威胁和通过共享管理和问责制在系统层面上创建偏见免疫的有效性。这项研究预期的产品类型在理论上正在进步（阐明新的威胁，对动力学的新理解处理）、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根据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智力优点和更广泛的影响进行评估，被认为值得支持审查标准。

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