_:n6921723ffdd64b259fcfb202929b506axb65
<http://schema.org/description>
"In artificial intelligence (AI) mediated workforce management systems (e.g.,\ncrowdsourcing), long-term success depends on workers accomplishing tasks\nproductively and resting well. This dual objective can be summarized by the\nconcept of productive laziness. Existing scheduling approaches mostly focus on\nefficiency but overlook worker wellbeing through proper rest. In order to\nenable workforce management systems to follow the IEEE Ethically Aligned Design\nguidelines to prioritize worker wellbeing, we propose a distributed\nComputational Productive Laziness (CPL) approach in this paper. It\nintelligently recommends personalized work-rest schedules based on local data\nconcerning a worker's capabilities and situational factors to incorporate\nopportunistic resting and achieve superlinear collective productivity without\nthe need for explicit coordination messages. Extensive experiments based on a\nreal-world dataset of over 5,000 workers demonstrate that CPL enables workers\nto spend 70\nmore ethically aligned scheduling than existing approaches."
<https://deepai.org/profile/lizhen-cui>