FW-HTF-R: Human-Machine Teaming for Effective Data Work at Scale: Upskilling Defense Lawyers Working with Police and Court Process Data

FW-HTF-R：大规模有效数据工作的人机协作：提高辩护律师处理警察和法院流程数据的技能

• 批准号: 2129008
• 负责人: Aditya Parameswaran
• 金额: $ 200万
• 依托单位: University of California-Berkeley
• 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 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2129008&HistoricalAwards=false
• 关键词: FW; HTF; Human; Machine; Teaming

This project will build tools to help defense attorneys do their work -- in particular, to help them use and understand the large quantities of data that they are now asked to handle. As more and more data about policing, courts, and individual cases becomes available, attorneys are finding that the evidence they need to advocate for their clients is locked in vast piles of messy, incomplete data. With the relevant information scattered across scans of hundreds of pages of paper forms or hours of audio and video, defenders do not have the programming and data analysis skills they need to extract key information from the public and private data at their disposal. This leaves defense attorneys at a disadvantage, particularly public defenders who have limited access to staff with data analysis expertise and who face high caseloads that leave them limited time to learn data analysis. To help address this gap, the project team will partner with legal associations and defense attorneys to develop data analysis methods and tools that do much of the work of collecting, organizing, and suggesting analyses of these messy police and court process data. Doing this will reduce the burden for defense attorneys, increase the value of data, and ultimately lead to fairer, better outcomes in criminal justice contexts.This project's data platform will leverage three key underlying techniques the project team will advance: (i) familiar no-code and low-code modalities like natural language search boxes and spreadsheet interfaces; (ii) program synthesis and machine learning to transform "fuzzy" queries in no-code interfaces into a space of possible interpretations (including improving predictions by generalizing from prior tool usage data); and (iii) interactive ambiguity resolution widgets that present visual representations of output data, allowing users to steer the tool towards their target programs or analyses by disambiguating between alternatives generated in (ii). In developing this platform, the team will contribute advances in program synthesis and ML-aided program generation, including novel algorithms for synthesis; develop novel mechanisms and algorithms for learning from users' prior activity in the context of data work tools; and invent new program recommendation algorithms, especially for recommending plausible tweaks to existing data analysis programs. These techniques will be incorporated into a larger user-centered design process toward building tools and interfaces that meet public defenders’ needs and take into account the legal context and constraints in which they work. The tools will be iteratively developed and evaluated among an increasingly large set of users, starting with individual defenders and public defenders’ offices, with the goal of producing off-the-shelf solutions that can be adopted by a range of legal entities and organizations. Together, the work will contribute to knowledge of how to build no-code and low-code tools to democratize data access more broadly.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.

这个项目将建立工具来帮助辩护律师做他们的工作-特别是，帮助他们使用和理解他们现在被要求处理的大量数据。随着越来越多的关于警务、法院和个案的数据变得可用，律师们发现，他们为客户辩护所需的证据被锁在大量混乱、不完整的数据中。由于相关信息分散在数百页的纸质表格或数小时的音频和视频扫描中，维护者不具备从其掌握的公共和私人数据中提取关键信息所需的编程和数据分析技能。这使得辩护律师处于不利地位，尤其是公设辩护人，他们接触具有数据分析专业知识的工作人员的机会有限，并且面临着大量的案件，这使得他们学习数据分析的时间有限。为了帮助解决这一差距，项目团队将与法律的协会和辩护律师合作，开发数据分析方法和工具，这些方法和工具可以完成收集、组织和建议分析这些混乱的警察和法院流程数据的大部分工作。这样做将减轻辩护律师的负担，增加数据的价值，并最终导致刑事司法环境中更公平，更好的结果。该项目的数据平台将利用项目团队将推进的三个关键基础技术：（i）熟悉的无代码和低代码模式，如自然语言搜索框和电子表格界面;（ii）程序合成和机器学习，将无代码接口中的“模糊”查询转换为可能的解释空间（包括通过从先前的工具使用数据进行归纳来改进预测）;以及（iii）交互式歧义消解窗口小部件，其呈现输出数据的视觉表示，允许用户通过消除（ii）中生成的备选项之间的歧义来将工具导向他们的目标程序或分析。 在开发这个平台的过程中，该团队将在程序合成和ML辅助程序生成方面做出贡献，包括用于合成的新算法;开发新的机制和算法，用于在数据工作工具的背景下从用户的先前活动中学习;并发明新的程序推荐算法，特别是用于推荐对现有数据分析程序的合理调整。这些技术将被纳入一个更大的以用户为中心的设计过程，以建立满足公设辩护人需要的工具和界面，并考虑到他们工作的法律的背景和限制。这些工具将在越来越多的用户中反复开发和评估，从个人辩护人和公设辩护人办公室开始，目标是产生可被一系列法律的实体和组织采用的现成解决方案。该奖项反映了NSF的法定使命，并通过使用基金会的智力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

A Need-Finding Study with Users of Geospatial Data

• DOI: 10.1145/3544548.3581370
• 发表时间: 2023-04
• 期刊: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
• 作者: Parker Ziegler;Sarah E. Chasins

Co-Designing for Transparency: Lessons from Building a Document Organization Tool in the Criminal Justice Domain

共同设计透明度：在刑事司法领域构建文档组织工具的经验教训

• DOI: 10.1145/3593013.3594093
• 发表时间: 2023
• 期刊: ACM
• 作者: Nigatu, Hellina Hailu;Pickoff-White, Lisa;Canny, John;Chasins, Sarah
• 通讯作者: Chasins, Sarah

Exploring the Learnability of Program Synthesizers by Novice Programmers

• DOI: 10.1145/3526113.3545659
• 发表时间: 2022-10
• 期刊: Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology
• 作者: Dhanya Jayagopal;Justin Lubin;Sarah E. Chasins

Trial by File Formats: Exploring Public Defenders' Challenges Working with Novel Surveillance Data

按文件格式进行审判：探索公设辩护人在使用新监控数据时面临的挑战

• DOI: 10.1145/3512914
• 发表时间: 2022
• 期刊: Proceedings of the ACM on Human-Computer Interaction
• 作者: Warren, Rachel B.;Salehi, Niloufar
• 通讯作者: Salehi, Niloufar