Algorithmic Management Of Digital Labor Platforms: Prospects And Challenges

Abstract

The advent of digital labor platforms has given rise to a new dimension of work, which includes algorithmic management of workforce to coordinate and actualize remote workforce across the globe. This paper explores the challenges and future opportunities of algorithmic management of workforce (crowd workers) in the digital labor platforms through comprehensive literature review and analysis of current practices. The key challenges such as transparency, fairness, accountability and worker autonomy are considered for the study, as these factors are considered as accelerator for a sustainable and ethical governance of digital labor platforms. The lack of HRM intervention, automated evaluation and task distribution of algorithmic management leads to ethical unfairness and distrust among platform labors or crowd workers. Further, the proper balance between effectiveness and worker well-being remains a recurring problem since algorithmic systems give priority to output, possibly at the cost of fair labor standards. Notwithstanding these challenges, algorithmic management has great promise for improving scalability, streamlining processes, and enabling flexible work schedules. This study suggests methods for enhancing algorithmic management frameworks by incorporating worker feedback channels, ethical norms, and transparency. Tackling these important issues can create more robust and fair digital labor ecosystems. The purpose of this paper is to add to the existing discussion on algorithmic management in digital labor platforms in a responsible manner, bringing attention to the transformative potential as well as the inherent obstacles.