AI Support for Improving Managerial Decision-making Processes Regarding Workforce Planning

Abstract

The accelerating integration of AI into the labor market is transforming the foundational dynamics of modern work, particularly through the rise of remote work and the expansion of the gig economy. Freelance platforms increasingly rely on algorithmic management systems that automate task allocation, performance monitoring, and disciplinary decisions. While these systems promise substantial productivity gains, they also introduce new challenges, including heightened information asymmetry and reduced transparency for workers who often lack mechanisms to contest automated outcomes. This disconnect has significant implications for worker well‑being, especially as critical human–resource functions become fully automated. This study proposes a methodological framework to leverage AI — specifically Natural Language Understanding — to support more transparent and equitable managerial decision making without compromising platform efficiency. Unlike conventional HR literature that assumes human involvement in conflict resolution, freelancers frequently encounter automated decisions that overlook nuanced indicators of distress. By incorporating computational techniques capable of detecting linguistic signals associated with emotional strain, this research addresses a critical gap in understanding how algorithmic systems can be designed to better account for human well‑being. The study develops an empirically grounded infrastructure for processing unstructured worker generated text, integrating sentiment analysis through the “Valence Aware Dictionary and Sentiment Reasoner” model and discourse interpretation via a discourse relation classifier trained on the Penn Discourse Treebank. Forming a departure point to enable automated sensitivity analysis of worker feedback by discovering a strong indicator out of discourse relation type categories is the sole artefact of the research that could lead towards the proof of concept about automated sensitivity analysis of worker feedback to sustain a scalable pathway toward human centered algorithmic management.

Keywords: Natural Language Understanding, Artificial Intelligence, Natural Language Model, Sentiment Analysis

DOI: 10.54941/ahfe1007607