[Paper Review] The future of human-AI collaboration: a taxonomy of design knowledge for hybrid intelligence systems

Dominik Dellermann, Adrian Calma+4 more|arXiv (Cornell University)|May 7, 2021

Big Data and Business Intelligence34 citations

TL;DR

This paper presents a taxonomy of design knowledge for hybrid intelligence systems that combine human and artificial intelligence and offers guidance for developers.

ABSTRACT

Recent technological advances, especially in the field of machine learning, provide astonishing progress on the road towards artificial general intelligence. However, tasks in current real-world business applications cannot yet be solved by machines alone. We, therefore, identify the need for developing socio-technological ensembles of humans and machines. Such systems possess the ability to accomplish complex goals by combining human and artificial intelligence to collectively achieve superior results and continuously improve by learning from each other. Thus, the need for structured design knowledge for those systems arises. Following a taxonomy development method, this article provides three main contributions: First, we present a structured overview of interdisciplinary research on the role of humans in the machine learning pipeline. Second, we envision hybrid intelligence systems and conceptualize the relevant dimensions for system design for the first time. Finally, we offer useful guidance for system developers during the implementation of such applications.

Motivation & Objective

• Provide a structured overview of interdisciplinary research on the role of humans in the machine learning pipeline.
• Envision hybrid intelligence systems and conceptualize the relevant design dimensions for these systems.
• Offer practical guidance for developers during the implementation of hybrid intelligence applications.

Proposed method

• Apply a taxonomy development method to synthesize interdisciplinary research.
• Present a structured overview of the role of humans in the machine learning pipeline.
• Conceptualize the design dimensions relevant for hybrid intelligence system development.
• Provide actionable guidance for system developers during implementation.

Experimental results

Research questions

• RQ1 What are the relevant design dimensions for hybrid intelligence systems?
• RQ2 How can human roles be integrated into the machine learning pipeline to improve outcomes?
• RQ3 What guidance can be offered to developers for implementing hybrid intelligence applications?
• RQ4 How can socio-technological ensembles of humans and machines be structured to achieve superior results?

Key findings

• Provide a structured overview of interdisciplinary research on the human role in the machine learning pipeline.
• Conceptualize and articulate the design dimensions needed for hybrid intelligence system design.
• Offer practical guidance for developers to implement and deploy hybrid intelligence applications.