The Human Side of Algorithmic Management: Exploring How Employees Negotiate AI-Driven Performance Monitoring, Autonomy, Privacy, and Workplace Trust

Abstract

This study explores how employees experience and negotiates digital performance monitoring and algorithm-supported management in Pakistani workplaces, focusing on autonomy, privacy, fairness, and workplace trust. An exploratory qualitative design was employed, drawing on condensed summaries of semi-structured online interviews with 22 participants across diverse occupational and sectoral settings. Reflexive thematic analysis identified six interrelated themes: negotiating visibility, autonomy under metrics, privacy boundaries, supervisor-dependent fairness, organizational rules and occupational responsibilities, and employee voice and opportunities for challenge. Findings indicate that participants generally acknowledged monitoring benefits, including improved coordination, clearer performance expectations, and compliance support, while expressing concerns about inadequate contextual evaluation, behavioural pressures, unclear data practices, and limited opportunities to contest assessments. A central insight is the distinction between supportive managerial relationships and dependable organizational safeguards. Although supervisors may provide explanations and contextual consideration, employees also require transparent review authority, recognized correction procedures, clear privacy boundaries, and accessible channels for raising concerns. The study contributes an exploratory, employee-centred perspective on responsible algorithmic management and offers practical recommendations for more transparent, context-sensitive, and accountable workplace monitoring.

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