Despite the considerable gains in human resource efficiency brought by automated oversight, employee trust has been deeply compromised due to algorithmic overreach. This crisis manifests as perceived unfairness from algorithmic black boxes, implicit discrimination driven by data bias, privacy anxiety from panoptic surveillance, and psychological contract rupture. Drawing on organizational behavior and technological ethics, this paper dissects the underlying structural dilemma: cognitive constraints caused by black boxes, collapsed fairness due to contextual blind spots, and power imbalances resulting from ubiquitous surveillance and a lack of institutional remedies. To resolve this crisis, enterprises must abandon pure technological instrumentalism. We propose a four-dimensional optimization framework: enhancing algorithmic transparency and auditing, establishing a "human-machine collaboration" decision-making mechanism, refining democratic consultation and rights remedy systems, and cultivating a human-centric digital ethical culture. Ultimately, this study seeks to integrate machine precision with human empathy, providing theoretical and practical guidance for repairing psychological contracts and constructing harmonious labor relations in the digital economy.

Shuang Wu. Unpacking the Trust Crisis in Human Resource Decision-Making under Algorithmic Management: Deep Causes and Optimization Paths. Academic Journal of Business & Management (2026), Vol. 8, Issue 4: 88-94. https://doi.org/10.25236/AJBM.2026.080412.

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