Trust in hiring algorithms: causal effect of beliefs about humans’ discrimination for workers, and self-confidence for managers

We run an online experiment to study ways to overcome algorithm aversion. We use a 2*2 design. Participants are either in the role of workers or of managers. They will furthermore either be assigned to the baseline or the treatment. Workers perform three real-effort tasks: task 1, task 2 and the job task. They must choose whether they prefer hiring decisions between themsef and another worker to be made by a participant in the role of a manager or an algorithm. They will get paid if they are hired in one random pair of workers they belong to. They know that the manager will get paid if they hire the best worker at the job task out of a random pair of workers they have made a hiring decision for. The managers and the algorithm will base their hiring decision on the genders of the workers and their task 1 and task 2 perfromances. In the workers' treatment, before deciding between the manager and the algorithm, workers are informed of the proportion of men and women who were chosen to be hired by the managers in a managers' session. Managers must make 20 hiring decisions between pairs of workers. They can see the gender of the workers and their task 1 and task 2 performances. We elicit for how many of the 20 decisions they believe they have hired the best worker of the pair. They must then decide whether they want to delegate the hiring decisions to the algorithm. If they do, their payoff will depend on the algorithm's hiring decision, not theirs. In the managers' treatment, managers will get a feedback about their over/under or well-calibrated confidence before the decision whether to delegate the hiring decisions to the algorithm