The Inherent Logic and Optimization Path of Embedding Adaptive Response Algorithms in Labor Dispute Governance

DOI

10.2991/978-94-6239-701-9_95How to use a DOI?

Keywords

Adaptive response algorithm; labor dispute governance; algorithmic bias; multi-party co-governance

Abstract

This paper analyzes the inherent logic of embedding adaptive response algorithms into labor dispute governance, pointing out that it promotes a paradigm shift from rigid efficiency to intrinsic compliance, from lagging regulation to agile collaboration, and from workers to co-builders. The study finds that current governance practices still face real dilemmas such as data barriers and algorithmic biases under platform dominance, ambiguity in responsibility caused by algorithmic black boxes, and hidden exploitation concealed by efficiency miraculous effects. This paper proposes constructing a visualized and explainable algorithmic system, improving a multi-party collaborative digital governance framework, and adhering to the core principles of “people-centeredness” and “human-in-the-loop,” thereby resolving digital injustice through technological architecture reshaping and power structure checks and balances.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jiacheng Qi
PY  - 2026
DA  - 2026/07/30
TI  - The Inherent Logic and Optimization Path of Embedding Adaptive Response Algorithms in Labor Dispute Governance
BT  - Proceedings of the 2026 11th International Conference on Social Sciences and Economic Development (ICSSED 2026)
PB  - Atlantis Press
SP  - 933
EP  - 939
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-701-9_95
DO  - 10.2991/978-94-6239-701-9_95
ID  - Qi2026
ER  -