  With the industry trend of shifting from a traditional hierarchical approach
to flatter management structure, crowdsourced performance assessment gained
mainstream popularity. One fundamental challenge of crowdsourced performance
assessment is the risks that personal interest can introduce distortions of
facts, especially when the system is used to determine merit pay or promotion.
In this paper, we developed a method to identify bias and strategic behavior in
crowdsourced performance assessment, using a rich dataset collected from a
professional service firm in China. We find a pattern of "discriminatory
generosity" on the part of peer evaluation, where raters downgrade their peer
coworkers who have passed objective promotion requirements while overrating
their peer coworkers who have not yet passed. This introduces two types of
biases: the first aimed against more competent competitors, and the other
favoring less eligible peers which can serve as a mask of the first bias. This
paper also aims to bring angles of fairness-aware data mining to talent and
management computing. Historical decision records, such as performance ratings,
often contain subjective judgment which is prone to bias and strategic
behavior. For practitioners of predictive talent analytics, it is important to
investigate potential bias and strategic behavior underlying historical
decision records.

Machine Learning;Econometrics;Machine Learning