Collaborative Research: DASS: Enabling Standards- and Disclosure-Based Regulations in and through Software Systems: Making Algorithmic Work Management Software Accountable to Law

软件系统已经成为公共和私营部门管理不可或缺的一部分，帮助和自动化关键的人类决策，如选人和分配资源。新出现的证据表明，用于算法管理的软件系统会严重破坏员工的福祉，可能不适合培养对现有劳动法的问责。例如，仓库工人由于任务分配和跟踪而没有适当的休息时间，承受着严重的身体和心理压力。按需乘车司机认为自动评估是不公平的，并不信任该系统不透明的支付计算，这导致工人因工资过低而提起诉讼。轮班员工的工作时间不可预测，这会破坏工作与生活的平衡，破坏他们提前计划的能力。同时，目前还没有一个既定的机制来规范这类软件系统。例如，对于如何在软件系统中应用公平的概念，没有专家达成共识。现有的工作法没有跟上新兴工作形式的步伐，比如算法管理和数字劳工平台，它们给工人带来了新的风险，包括工作时间表的波动和雇主对工人在岗和下班的监控。为了应对这些挑战，我们的目标是开发能够(1)使软件对现有法律负责，以及(2)通过衡量某些软件使用和行为的负面影响来解决现有法律中的漏洞的技术方法，以帮助利益相关者更好地缓解这些影响。换句话说，我们的目标是让软件对法律和政策负责，并利用它让软件用户(个人和公司)对受影响的人群和公众负责。该项目正在开发新的方法，以便在整个软件开发周期中利用正式方法、人机交互、社会学、公共政策和法律，在软件系统中和通过软件系统实现基于标准和披露的监管。这项工作将专注于算法工作调度，这将影响占美国工人总数25%的倒班工人。它将采取参与性办法，让利益攸关方、公共政策和法律专家、政府、商业软件公司以及公司中的软件用户和那些受软件使用影响的人参与软件设计和评估。这项研究将在算法调度的背景下进行三个方面的推进：(1)监管软件需求的参与式形式化，(2)可扩展和交互的形式化方法和软件保证和决策支持的自动推理，以及(3)监管结果评估和监测。通过开发负责任的调度软件，该项目有可能产生重大的、更广泛的影响，因为它为企业提供遵守和问责现有工作调度规则所需的工具，以及在其业务运营中提供更多时间表稳定性和可预测性的能力。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Software systems have become an integral part of public and private sector management, assisting and automating critical human decisions such as selecting people and allocating resources. Emerging evidence suggests that software systems for algorithmic management can significantly undermine workforce well-being and may be poorly suited to fostering accountability to existing labor law. For example, warehouse workers are under serious physical and psychological stress due to task assignment and tracking without appropriate break times. On-demand ride drivers feel that automated evaluation is unfair and distrust the system’s opaque payment calculations which has led to worker lawsuits for wage underpayment. Shift workers suffer from unpredictable schedules that destabilize work-life balance and disrupt their ability to plan ahead. Meanwhile, there is not yet an established mechanism to regulate such software systems. For example, there is no expert consensus on how to apply concepts of fairness in software systems. Existing work laws have not kept pace with emerging forms of work, such as algorithmic management and digital labor platforms that introduce new risk to workers, including work-schedule volatility and employer surveillance of workers both on and off the job. To tackle these challenges, we aim to develop technical approaches that can (1) make software accountable to existing law, and (2) address the gaps in existing law by measuring the negative impacts of certain software use and behavior, so as to help stakeholders better mitigate those effects. In other words, we aim to make software accountable to law and policy, and leverage it to make software users (individuals and firms) accountable to the affected population and the public. This project is developing novel methods to enable standards and disclosure-based regulation in and through software systems drawing from formal methods, human-computer interaction, sociology, public policy, and law throughout the software development cycle. The work will focus on algorithmic work scheduling, which impacts shift workers who make up 25% of workers in the United States. It will take a participatory approach involving stakeholders, public policy and legal experts, governments, commercial software companies, as well as software users in firms and those affected by the software’s use, in the software design and evaluation. The research will take place in three thrusts in the context of algorithmic scheduling: (1) participatory formalization of regulatory software requirements, (2) scalable and interactive formal methods and automated reasoning for software guarantees and decision support, and (3) regulatory outcome evaluation and monitoring. By developing accountable scheduling software, the project has the potential for significant broader impacts by giving businesses the tools they need for compliance with and accountability to existing work scheduling regulations, as well as the capacity to provide more schedule stability and predictability in their business operations.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.