Human computation systems are characterized by the use of human workers to solve computationally difficult problems. Expertise profiling involves assessment and representation of a worker’s expertise, in order to route human computation tasks to appropriate workers. This paper studies the relationship between the assessment workload on workers and the quality of task routing. Three expertise assessment approaches were compared with the help of a user study, using two different groups of human workers. The first approach requests workers to provide self-assessment of their knowledge. The second approach measures the knowledge of workers through their performance against tasks with known responses. We propose a third approach based on a combination of self-assessment and task-assessment. The results suggest that the self-assessment approach requires minimum assessment workload from workers during expertise profiling. By comparison, the task-assessment approach achieved the highest response rate and accuracy. The proposed approach requires less assessment workload, while achieving the response rate and accuracy similar to the task-assessment approach.