Dignity and use of algorithm in performance evaluation

Zhang, Lixuan; Amos, Clinton

doi:10.1080/0144929x.2022.2164214

Cited by 4 publications

(1 citation statement)

References 84 publications

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“…In addition, the paper by Tsai et al (2022) discussed human-robot work, primarily using the notion of collaboration to explore different roles that robots can take, including follower, partner, or leader. The paper by Zhang and Amos (2023) focused on collaboration between humans and algorithms. Fogliato et al (2022) focused on "AI-assisted decisionmaking" (p. 1362) and used mainly the term "collaboration" to describe the form of interaction.…”

Section: Understandings Of Human-ai Teamingmentioning

confidence: 99%

Defining human-AI teaming the human-centered way: a scoping review and network analysis

Berretta,

Tausch,

Ontrup

et al. 2023

Front. Artif. Intell.

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IntroductionWith the advancement of technology and the increasing utilization of AI, the nature of human work is evolving, requiring individuals to collaborate not only with other humans but also with AI technologies to accomplish complex goals. This requires a shift in perspective from technology-driven questions to a human-centered research and design agenda putting people and evolving teams in the center of attention. A socio-technical approach is needed to view AI as more than just a technological tool, but as a team member, leading to the emergence of human-AI teaming (HAIT). In this new form of work, humans and AI synergistically combine their respective capabilities to accomplish shared goals.MethodsThe aim of our work is to uncover current research streams on HAIT and derive a unified understanding of the construct through a bibliometric network analysis, a scoping review and synthetization of a definition from a socio-technical point of view. In addition, antecedents and outcomes examined in the literature are extracted to guide future research in this field.ResultsThrough network analysis, five clusters with different research focuses on HAIT were identified. These clusters revolve around (1) human and (2) task-dependent variables, (3) AI explainability, (4) AI-driven robotic systems, and (5) the effects of AI performance on human perception. Despite these diverse research focuses, the current body of literature is predominantly driven by a technology-centric and engineering perspective, with no consistent definition or terminology of HAIT emerging to date.DiscussionWe propose a unifying definition combining a human-centered and team-oriented perspective as well as summarize what is still needed in future research regarding HAIT. Thus, this work contributes to support the idea of the Frontiers Research Topic of a theoretical and conceptual basis for human work with AI systems.

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Section: Understandings Of Human-ai Teamingmentioning

confidence: 99%

Defining human-AI teaming the human-centered way: a scoping review and network analysis

Berretta,

Tausch,

Ontrup

et al. 2023

Front. Artif. Intell.

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This (AI)n’t fair? Employee reactions to artificial intelligence (AI) in career development systems

Köchling,

Wehner,

Ruhle

2024

Rev Manag Sci

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Organizations increasingly implement AI for career development to enhance efficiency. However, there are concerns about employees’ acceptance of AI and the literature on employee acceptance of AI is still in its infancy. To address this research gap, integrating justice theory, we investigate the effects of the deciding entity (human, human and AI, and AI) and the impact of the data source (internal data, external data), on employees’ reactions. Using a scenario-based between-subject design, displaying a common situation in organizations (N = 280) and an additional causal-chain-approach (N = 157), we examined whether a decrease of human involvement in decision making diminishes employees’ perceived fairness and satisfaction with the career development process and increases their perceived privacy intrusion. Although we also considered other data sources to moderate the proposed relationships, we found no support for interaction effects. Finally, fairness and privacy intrusion mediated the influence of the deciding entity and data source on turnover intention and employer attractiveness, while satisfaction with the process did not. By addressing how the employees react to AI in career development–showing the negative reactions, our study holds considerable relevance for research and practice.

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Augmenting Human Decision-Making in K-12 Education: The Role of Artificial Intelligence in Assisting the Recruitment and Retention of Teachers of Color for Enhanced Diversity and Inclusivity

Sadeghi,

Niu

2024

Leadership and Policy in Schools

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Dignity and use of algorithm in performance evaluation

Cited by 4 publications

References 84 publications

Defining human-AI teaming the human-centered way: a scoping review and network analysis

Defining human-AI teaming the human-centered way: a scoping review and network analysis

This (AI)n’t fair? Employee reactions to artificial intelligence (AI) in career development systems

Augmenting Human Decision-Making in K-12 Education: The Role of Artificial Intelligence in Assisting the Recruitment and Retention of Teachers of Color for Enhanced Diversity and Inclusivity

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