Rethinking People Analytics With Inverse Transparency by Design

Abstract

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 software 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.

Data Owner Benefit-Driven Design of People Analytics

“…Regarding PA, this indicates two possible ways of influencing the disclosure decision in favor of sharing data: By decreasing the perceived risks, e.g. by giving data owners greater transparency regarding how their data is used [41] or by increasing the benefits of a disclosure, e.g. through the use of appeal strategies such as incentives [40].…”

“…With respect to privacy concerns, this informed consent can raise acceptance for analyses due to additional transparency [17,36]. Given these points, data owners can be seen as data sovereigns that are in charge of their data and must be convinced of its usage [41], which is where benefits [40] come into play.…”

“…This privacy calculus means that two approaches exist to increase employees' willingness to share: decreasing the risks, e.g. by providing additional transparency [41], or increasing the appeal of the decision. To accomplish the latter, Zieglmeier et al [40] define three types of appeal strategies: values, incentives, and benefits.…”