About

Mohammad Hossein Jarrahi is a Professor at the University of North Carolina at Chapel Hill, specializing in the study of artificial intelligence (AI) and its impact on work. His research adopts a sociotechnical perspective to explore the dynamic interactions between technology, people, and organizational contexts. He has focused on how AI transforms knowledge work by reshaping work practices and organizational routines. A central theme in his work is the concept of 'human-AI symbiosis,' which highlights the collaborative potential between humans and AI systems to enhance decision-making and problem-solving within organizations. Additionally, he has contributed to the understanding of algorithmic management by examining how algorithms are used to automate or augment managerial functions. Earlier in his career, Jarrahi investigated flexible organizational contexts such as the gig economy, analyzing the dual roles of digital labor platforms in structuring and mediating work practices. Jarrahi's academic career includes positions at the University of North Carolina at Chapel Hill, where he has progressed from Assistant Professor to full Professor in the iSchool. Prior to this, he held roles at Syracuse University in the School of Information Studies, including Instructor and Research Assistant. His educational background includes a PhD in Information Science and Technology from Syracuse University, a Master of Information Systems from the London School of Economics and Political Science, and a Bachelor of Science in Public Administration from Shahdi Beheshti University.

Research topics

• Computer Science
• Sociology
• Artificial Intelligence
• Knowledge management
• Engineering
• Data science
• Mathematics
• Management science
• Political Science
• Epistemology
• Social psychology
• Marketing
• Psychology
• Business

Selected publications

• Anthropomorphic Behaviors of AI

  SSRN Electronic Journal · 2026-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Preserving Identity through Innovation: AI-Based Digital Preservation of Uzbek Pile Weaving Traditions

  Open MIND · 2026-01-01

  articleOpen accessSenior author
  OA PDFDOI

• Anthropomorphic Behaviors of AI

  2026-01-01

  articleOpen access
  PublisherDOI

• What Human-Horse Interactions May Teach Us About Effective Human-AI Interactions

  UNC Libraries · 2026-01-06

  articleOpen access1st authorCorresponding

  AI systems are progressively making inroads into more application contexts. There is a growing consensus, however, that replacing humans with specialized expertise and implicit knowledge with AI systems may not be beneficial. Some conventional benchmarks for AI, such as the Turing test, might not fully capture the roles AI can play alongside human intelligence since they encourage AI to mimic and implicitly replace human intelligence rather than complement it. Instead of full human emulation by AI, humans should remain “in the loop” in many processes, suggesting that the future of computing depends on a collaborative partnership between human and AI systems rather than a competition. But the question remains: How can this partnership be envisioned, considering the different types of intelligence that humans and AI possess?

  PublisherDOI

• Algorithmic management in a work context

  UNC Libraries · 2026-04-08

  articleOpen access

  The rapid development of machine-learning algorithms, which underpin contemporary artificial intelligence systems, has created new opportunities for the automation of work processes and management functions. While algorithmic management has been observed primarily within the platform-mediated gig economy, its transformative reach and consequences are also spreading to more standard work settings. Exploring algorithmic management as a sociotechnical concept, which reflects both technological infrastructures and organizational choices, we discuss how algorithmic management may influence existing power and social structures within organizations. We identify three key issues. First, we explore how algorithmic management shapes pre-existing power dynamics between workers and managers. Second, we discuss how algorithmic management demands new roles and competencies while also fostering oppositional attitudes toward algorithms. Third, we explain how algorithmic management impacts knowledge and information exchange within an organization, unpacking the concept of opacity on both a technical and organizational level. We conclude by situating this piece in broader discussions on the future of work, accountability, and identifying future research steps.

  PublisherDOI

• Circular Economy and Intangible Cultural Heritage: AI-Enabled Pathways for Sustainable Textile Arts

  Open MIND · 2026-01-01

  articleOpen accessSenior author
  OA PDFDOI

• Hybrid Intelligence

  2026-01-01

  book-chapterSenior author
  PublisherDOI

• Artificial intelligence and knowledge management: A partnership between human and AI

  UNC Libraries · 2025-03-20 · 1 citations

  articleOpen access
  PublisherDOI

• The Principles of Data-Centric AI

  UNC Libraries · 2025-04-04

  articleOpen access1st authorCorresponding

  Uniting data-centric perspectives and concepts to trace the foundations of DCAI.

  PublisherDOI

• Algorithmic Management in Limbo: Task‐Driven Interweaving of Hierarchy and Market Management

  Human Resource Management · 2025-09-04 · 2 citations

  articleOpen access

  ABSTRACT The growing use of algorithmic management (AM) in human resource (HR) activities has attracted growing attention from HR scholars, as organizations increasingly rely on digital labor platforms to leverage external workers. This study examines how these platforms apply AM in human resource management (HRM) and how these algorithmic systems embed both market and hierarchy management principles for shaping worker control and autonomy. Specifically, we seek to examine how AM manifests in two distinct ways across these platforms: one involving hierarchy and control, and another involving matching and autonomy. Using an inductive qualitative design, we analyzed 33 semi‐structured interviews with platform workers and documentary data from 23 digital labor platforms. Whereas prior research often frames AM in binary terms—that is, market/autonomy versus hierarchy/control—we explore how task characteristics influence the joint application of both market and hierarchy principles in AM for HRM activities of digital labor platforms. Our findings show how platforms dynamically calibrate market and hierarchy approaches to AM in response to task demands, balancing flexibility, oversight, discretion, and incentives. For HR scholars, this study highlights the flexible and conditional nature of AM systems that blend autonomy and control in nuanced ways. By moving beyond the dominant autonomy‐versus‐control dichotomy, we show how AM is configured to align with diverse forms of work across platforms, enhancing efficiency while sustaining worker engagement amid evolving task demands.

  PublisherOA PDFDOI

Frequent coauthors

• Steve Sawyer

  Syracuse University

  17 shared

• Will Sutherland

  University of Washington

  12 shared

• Sarah Beth Nelson

  University of Wisconsin–Milwaukee

  12 shared

• Leslie Thomson

  9 shared

• Ingrid Erickson

  Syracuse University

  8 shared

• Christoph Lutz

  BI Norwegian Business School

  6 shared

• Grace Shin

  Sookmyung Women's University

  6 shared

• Dorothy Lee Blyth

  University of North Carolina at Chapel Hill

  5 shared

Labs

• Equity in Making (EITM) LabPI

Awards & honors

• 2019 – Finalist, Lee Dirks Best Paper Award, iConference 201…
• 2018 – Best Article Award, Business Horizons, “Artificial in…
• 2017 – Tanner Award for Excellence in Undergraduate Teaching…
• 2015 – Deborah Barreau Award for Teaching Excellence, Univer…