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Article

Human-AI Collaboration in Urban Digital Transformation: Balancing Efficiency and Human Values in Shanghai’s AI-Enabled Talent Recruitment

Ting Li ORCID ,  Salme Yahya ORCID
    Received: 10 April 2026; Revised: 27 April 2026; Accepted: 7 May 2026; Published: 2 November 2026

    Abstract

    In the context of the urban digital revolution, recruitment through AI technologies has become common practice to address competition in the labor market. However, it is still unclear how the effectiveness of such recruitment is connected with humanistic values. For this research, an exploratory qualitative research design was used, involving semi-structured interviews with eight human resource professionals in Shanghai. Based on the results, the use of AI in recruitment led to increased efficiency mainly when screening candidates through high-volume, standardized processes, although the promised efficiency was not delivered in practice because the introduction of the AI system increased the workload. Patterns of human-machine cooperation were observed in different companies: tech companies and foreign companies are inclined to adopt interactive adjustment approaches, while state-owned enterprises retain human-dominated decision-making. Transparency of algorithms was still lacking in numerous instances, and there was an asymmetry in the accountability framework of algorithms, wherein disobeying algorithms carries more personal consequences than obeying them. The more experienced professionals became more autonomous in their work, while the less experienced professionals depended more on the algorithm. Issues like management support, information accuracy, and departmental integration have a major impact on implementation. This study contributes to the theory of human-machine collaboration by proposing the concept of asymmetric algorithmic accountability, which captures the uneven distribution of organizational consequences for following versus overriding AI recommendations and shapes practitioner behavior in ways that extend beyond individual trust or algorithmic transparency.

    Keywords

    References

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