From Human-Human Collaboration to Human-AI Collaboration: Designing AI Systems That Can Work Together with People

By Dakuo Wang, Elizabeth F. Churchill, Pattie Maes, Xiangmin Fan, Ben Shneiderman, Yuanchun Shi, Qianying Wang. CHI Extended Abstracts, 2020

343 citations recorded by the source corpus.

Artificial Intelligent (AI) and Machine Learning (ML) algorithms are coming out of research labs into the real-world applications, and recent research has focused a lot on Human-AI Interaction (HAI) and Explainable AI (XAI). However, Interaction is not the same as Collaboration. Collaboration involves mutual goal understanding, preemptive task co-management and shared progress tracking. Most of human activities today are done collaboratively, thus, to integrate AI into the already-complicated human workflow, it is critical to bring the Computer-Supported Cooperative Work (CSCW) perspective into the root of the algorithmic research and plan for a Human-AI Collaboration future of work. In this panel we ask: Can this future for trusted human-AI collaboration be realized? If so, what will it take? This panel will bring together HCI experts who work on human collaboration and AI applications in various application contexts, from industry and academia and from both the U.S. and China. Panelists will engage the audience through discussion of their shared and diverging visions, and through suggestions for opportunities and challenges for the future of human-AI collaboration.

Subfields: Artificial Intelligence, Health Informatics, Safety Research

View Paper: https://www.semanticscholar.org/paper/660a2244efa8af0b77fd314a1c75dcc01aa677fe

How researchers see this paper

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In Artificial Intelligence

  1. Lixiang Yan
  2. Dragan Gasevic

In Health Informatics

  1. Dragan Gasevic

In Safety Research

  1. Lixiang Yan
  2. Dragan Gasevic

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