Employees work in increasingly digital environments that enable advanced analytics. Yet, they lack oversight over the systems that process their data. That means that potential analysis errors or hidden biases are hard to uncover. Recent data protection legislation tries to tackle these issues, but it is inadequate. It does not prevent data misusage while at the same time stifling sensible use cases for data. We think the conflict between data protection and increasingly data-driven systems should be solved differently. When access to an employees' data is given, all usages should be made transparent to them, according to the concept of inverse transparency. This allows individuals to benefit from sensible data usage while addressing the potentially harmful consequences of data misusage. To accomplish this, we propose a new design approach for workforce analytics we refer to as inverse transparency by design. To understand the developer and user perspectives on the proposal, we conduct two exploratory studies with students. First, we let small teams of developers implement analytics tools with inverse transparency by design to uncover how they judge the approach and how it materializes in their developed tools. We find that architectural changes are made without inhibiting core functionality. The developers consider our approach valuable and technically feasible. Second, we conduct a user study over three months to let participants experience the provided inverse transparency and reflect on their experience. The study models a software development workplace where most work processes are already digital. Participants perceive the transparency as beneficial and feel empowered by it. They unanimously agree that it would be an improvement for the workplace. We conclude that inverse transparency by design is a promising approach to realize accepted and responsible people analytics.

翻译：员工在日益数字化的环境中工作，这为高级分析提供了条件。然而，他们缺乏对处理其数据的系统的监督。这意味着潜在的分析错误或隐藏偏差难以被发现。近期的数据保护立法试图解决这些问题，但收效不足。它既无法防止数据滥用，同时又抑制了数据的合理使用案例。我们认为，数据保护与日益数据驱动系统之间的冲突应以不同方式解决。当允许访问员工数据时，根据逆透明度的概念，所有使用情况都应向其透明化。这使得个人既能受益于合理的数据使用，又能应对数据滥用可能带来的有害后果。为此，我们提出了一种新的劳动力分析方法，称为“通过设计实现逆透明度”。为了了解开发者和用户对这一提案的看法，我们开展了两次探索性研究，参与者均为学生。首先，我们让小型开发团队以“通过设计实现逆透明度”的方式来实施分析工具，以揭示他们如何评价这一方法及其在开发工具中的具体体现。我们发现，在未影响核心功能的情况下，架构发生了改变。开发者认为我们的方法有价值且在技术上可行。其次，我们进行了一项为期三个月的用户研究，让参与者体验所提供的逆透明度并反思其经历。该研究模拟了一个大多数工作流程已实现数字化的软件开发工作场所。参与者认为透明度有益，并因此感到获得了赋能。他们一致认为，这将改善工作场所。我们得出结论，“通过设计实现逆透明度”是实现被接受且负责任的人员分析的一种有前景的方法。