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：员工日益在数字化环境中工作，这些环境支持高级分析技术。然而，他们对处理其数据的系统缺乏监督，这意味着潜在的分析错误或隐藏的偏见难以被发现。近期数据保护法规试图解决这些问题，但存在不足。它未能阻止数据滥用，同时抑制了合理的用例。我们认为，数据保护与日益数据驱动的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，根据逆透明度概念，所有使用情况都应向其透明化。这使个人既能受益于合理的数据使用，又能应对数据滥用的潜在危害。为实现这一目标，我们提出了一种新的劳动力分析设计方法，称为“通过设计实现逆透明度”。为了解开发者和用户对该提案的看法，我们开展了两次探索性学生研究。首先，我们让小型开发团队实施具有“通过设计实现逆透明度”的分析工具，以揭示他们如何评价该方法，以及该方法如何在其开发的工具中具体化。我们发现，在不抑制核心功能的情况下，架构发生了调整。开发者认为我们的方法具有价值且技术上可行。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体验。该研究模拟了一个大部分工作流程已数字化的软件开发环境。参与者认为透明度有益且感到被赋能。他们一致同意这将改善工作环境。我们得出结论：“通过设计实现逆透明度”是实现可接受且负责任的人员分析的一种有前景的方法。