Effective Human AI-Collaboration for the Boost in Productivity

Mazhar Mohsin (), Tapani Honkanen (), Muhammad Imran () and Sarah Zafar ()
Additional contact information
Mazhar Mohsin: Häme University of Applied Sciences, School of Business, Design and Technology
Tapani Honkanen: Häme University of Applied Sciences, School of Business, Design and Technology
Muhammad Imran: KIMEP University
Sarah Zafar: NUST Business School, National University of Sciences and Technology

A chapter in Artificial Humans, 2026, pp 183-202 from Springer

Abstract: Abstract Human-AI collaboration (HAC) is transforming various industries by integrating artificial intelligence into collaborative frameworks where AI systems and humans work together to achieve common goals. This paper is focused on understanding human-AI collaboration, starting with the fundamental principles and the frameworks or theories behind human-in-the-loop (HITL) and human-on-the-loop (HOTL) systems, decision-making augmentation, and collaborative reinforcement learning. It also describes the major technologies and tools that are employed in human-AI collaboration (HAC) in different sectors like healthcare, manufacturing, retail, and defense. The research also sheds light on different ethical, technical, and practical human-AI collaboration (HAC) challenges such as AI interpretability, trust, bias, and human cognitive load management. Lastly, the future of adaptive AI models and AI-powered decision-making frameworks along with human-centric AI applications are discussed.

Keywords: Human-AI collaboration; Decision-making augmentation; Explainable AI; Collaborative reinforcement learning; Human-in-the-loop systems (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:fuobcp:978-3-032-06604-6_11

Ordering information: This item can be ordered from
http://www.springer.com/9783032066046

DOI: 10.1007/978-3-032-06604-6_11

Access Statistics for this chapter

More chapters in Future of Business and Finance from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().