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Employees work in increasingly digital environments that enable advancedanalytics. 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 isinadequate. It does not prevent data misusage while at the same time stiflingsensible use cases for data.  We think the conflict between data protection and increasingly data-drivensystems should be solved differently. When access to an employees' data isgiven, all usages should be made transparent to them, according to the conceptof inverse transparency. This allows individuals to benefit from sensible datausage while addressing the potentially harmful consequences of data misusage.To accomplish this, we propose a new design approach for workforce analytics werefer to as inverse transparency by design.  To understand the developer and user perspectives on the proposal, we conducttwo exploratory studies with students. First, we let small teams of developersimplement analytics tools with inverse transparency by design to uncover howthey judge the approach and how it materializes in their developed tools. Wefind 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 theprovided inverse transparency and reflect on their experience. The study modelsa 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. Weconclude that inverse transparency by design is a promising approach to realizeaccepted and responsible people analytics.

Rethinking People Analytics With Inverse Transparency by Design