  In artificial intelligence (AI) mediated workforce management systems (e.g.,
crowdsourcing), long-term success depends on workers accomplishing tasks
productively and resting well. This dual objective can be summarized by the
concept of productive laziness. Existing scheduling approaches mostly focus on
efficiency but overlook worker wellbeing through proper rest. In order to
enable workforce management systems to follow the IEEE Ethically Aligned Design
guidelines to prioritize worker wellbeing, we propose a distributed
Computational Productive Laziness (CPL) approach in this paper. It
intelligently recommends personalized work-rest schedules based on local data
concerning a worker's capabilities and situational factors to incorporate
opportunistic resting and achieve superlinear collective productivity without
the need for explicit coordination messages. Extensive experiments based on a
real-world dataset of over 5,000 workers demonstrate that CPL enables workers
to spend 70% of the effort to complete 90% of the tasks on average, providing
more ethically aligned scheduling than existing approaches.
