# Ethically Aligned Opportunistic Scheduling for Productive Laziness

arXiv: https://arxiv.org/abs/1901.00298 · HTML: https://mlsanity.com/paper/1901.00298/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jan 2019.

## Built on by (most cited first, 2 of 2)

- [Hire When You Need to: Gradual Participant Recruitment for Auction-based Federated Learning](https://mlsanity.com/paper/2310.02651/) (2023)
- [Towards AI-Empowered Crowdsourcing](https://mlsanity.com/paper/2212.14676/) (2022)

## Abstract (arXiv)

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.