Job boards that suggest roles based on your 'clicks' are actually making your life worse.
March 24, 2026
Original Paper
A Job I Like or a Job I Can Get: Designing Job Recommender Systems Using Field Experiments
arXiv · 2603.21699
The Takeaway
Standard algorithms ignore the trade-off between how much a person likes a job and the probability of actually getting it. The study found that 'behavior-based' rankings are fundamentally suboptimal for worker welfare compared to a specific mathematical 'expected surplus' index.
From the abstract
Recommendation systems (RSs) are increasingly used to guide job seekers on online platforms, yet the algorithms currently deployed are typically optimized for predictive objectives such as clicks, applications, or hires, rather than job seekers' welfare. We develop a job-search model with an application stage in which the value of a vacancy depends on two dimensions: the utility it delivers to the worker and the probability that an application succeeds. The model implies that welfare-optimal RSs