Since algorithms are widely adopted by businesses and often require human involvement, understanding how humans perceive algorithms is instrumental to the success of algorithm design. Particularly, the growing concern that algorithms may reproduce inequality historically exhibited by humans calls for research about how people perceive the fairness of algorithmic decision-making relative to traditional human-based decision-making and, consequently, adjust their work behaviors. We study how algorithmic (vs. human-based) task assignment processes change task recipients’ fairness perceptions and productivity. In a 15-day-long field experiment with Alibaba Group in a warehouse where workers pick products following orders (or “pick lists”), we randomly assigned half of the workers to receive pick lists from a machine that ostensibly relied on an algorithm to distribute pick lists, and the other half to receive pick lists from a human distributor. Despite that we used the same underlying rule to assign pick lists in both groups, workers perceived the algorithmic (vs. human-based) assignment process as fairer by 0.94-1.02 standard deviations. This yielded productivity benefits: receiving tasks from an algorithm (vs. a human) increased workers’ picking efficiency by 17.35%-19.39%. The algorithmic assignment produced larger productivity gains among workers for whom perceived fairness has a stronger effect on productivity, including more educated workers and workers who cared more about the difficulty of their pick lists. We replicated the main results in another field experiment and showed via online experiments that people in the U.S. also view algorithmic task assignment as fairer. We demonstrate that algorithms can have broader impacts beyond offering greater efficiency and accuracy than humans: introducing algorithmic assignment processes may enhance fairness perceptions and productivity. This insight can be utilized by managers to better design and implement algorithm-based decision making at work.