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Influence of Leadership on Human-Artificial Intelligence Collaboration.

Rodrigo Zárate-Torres1, C Fabiola Rey-Sarmiento1, Julio César Acosta-Prado2

  • 1Colegio de Estudios Superiores de Administración-CESA, Bogotá 110311, Colombia.

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Summary

Leadership strategically mediates the relationship between human intelligence (HI) and artificial intelligence (AI). This model provides guidelines for ethical AI supervision and enhances human-AI collaboration in organizations.

Keywords:
artificial intelligencehuman intelligencehybrid interactionleadershiptechnological governance

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Area of Science:

  • Management Science
  • Technological Ethics
  • Cognitive Science

Background:

  • The integration of artificial intelligence (AI) into organizations necessitates understanding its interplay with human intelligence (HI).
  • Existing literature requires a framework to conceptualize leadership's role in the evolving HI-AI relationship.
  • Identifying research gaps in the dynamic between human and artificial intelligence is crucial for effective management.

Purpose of the Study:

  • To propose a conceptual model explaining leadership's influence on the human intelligence (HI) and artificial intelligence (AI) relationship.
  • To identify ethical and strategic mediation roles of leadership in hybrid human-AI cooperation.
  • To offer organizational guidelines for supervising AI-supported systems and enhancing human-AI interaction.

Main Methods:

  • A qualitative, non-systematic literature review was conducted.
  • Searches were performed in Scopus and Web of Science databases.
  • Boolean combinations of "leadership," "artificial intelligence," and "human intelligence" were used for literature published in the last 5 years.
  • Thematic analysis identified conceptual patterns and research gaps.

Main Results:

  • Leadership acts as an ethical and strategic mediator in the HI-AI relationship within a cooperative hybrid space.
  • Automated decisions are contextualized through human judgment and reasoning, supported by ethical governance mechanisms for AI.
  • Cognitive adaptability balances algorithmic efficiency, and leadership tools enhance the HI-AI relationship.
  • A model is proposed where leadership facilitates human-AI systems, creating flexible, efficient, and ethically overseen hybrid interactions.

Conclusions:

  • The conceptual model provides a framework for understanding leadership's dynamic role in facilitating human-AI collaboration.
  • Organizations can utilize the proposed guidelines for ethical AI supervision and strategic decision-making in complex environments.
  • The study integrates transdisciplinary knowledge, offering a novel perspective on ethical interrelationships in AI-assisted decision-making architectures.