# Ethically Aligned Opportunistic Scheduling for Productive Laziness

## TL;DR

This paper introduces a distributed scheduling approach that balances worker productivity and wellbeing by recommending personalized work-rest schedules, leading to ethically aligned and highly efficient workforce management.

## Contribution

It proposes a novel Computational Productive Laziness (CPL) method that personalizes work-rest schedules using local data, promoting worker wellbeing and superlinear collective productivity.

## Key findings

- Workers spend 70% effort to complete 90% tasks on average.
- CPL outperforms existing scheduling approaches in ethical alignment.
- Achieves superlinear productivity through opportunistic resting.

## Abstract

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.