Cover: Supporting the Royal Australian Navy's Campaign Plan for Robotics and Autonomous Systems

Supporting the Royal Australian Navy's Campaign Plan for Robotics and Autonomous Systems

Human-Machine Teaming and the Future Workforce

Published Jul 18, 2022

by Marigold Black, Linda Slapakova, Paola Fusaro, James Black

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Research Question

  1. What are the implications of RAS-AI and, more specifically, HMT on the RAN’s future workforce?

The Royal Australian Navy (RAN) is modernising its forces to better address the growing challenges faced by Australia in the Indo-Pacific region. This report provides an overview of the various impacts of robotics, autonomous systems, and artificial intelligence (RAS-AI) on the Defence workforce to inform the RAN's ongoing efforts to facilitate RAS-AI integration.

The authors draw on a review of relevant open-source academic and grey literature, with a focus on identifying possible lessons for the RAN. The analysis concentrated on the overall impacts of RAS-AI on the Defence workforce and skills, with a particular focus on the implications of human-machine teaming (HMT) for the Defence workforce.

The findings underscore the fundamentally different and novel way of working required to effectively adopt HMT. Integration of HMT into the workforce will require flexible management of complex personnel networks and continuous adaptation of existing structures and concepts.

This report is a continuation of work conducted in support of the RAN's RAS-AI Strategy 2040, released in 2020. RAND Australia was asked to provide policy analysis and advice to support development of an actionable RAS-AI Campaign Plan that could assist RAS-AI implementation efforts. The research team has examined three specific areas to support development of an actionable plan: military innovation, a missions and technology assessment for maritime RAS-AI, and HMT. This work should inform the RAN, other Australian Defence services, and Defence more broadly about the implications of HMT for the RAN's future workforce.

Key Findings

  • Normalisation of HMT in the RAN will require flexible management of the Defence workforce and continuous adaptation of existing structures and concepts.
  • HMT necessitates a shift in cognition as much as training and perception.
  • The goal in HMT is to optimise the interaction and leverage the strengths of both humans and machines.
  • HMT encompasses broad and complex issues and defies categorisation into an ineluctable laundry list of principles, activities and resources.
  • Understanding and considering the spectrum of human-machine interaction (HMI) is integral.
  • New technological developments should be aligned with actual problems/needs.
  • The skills/attributes required for HMT may not traditionally be prized by Defence.
  • Organisational learning must be embraced to deliver this challenging capability.
  • HMT should be leveraged not only as an effective military fighting capability but also as a concurrent training capability.
  • Success in HMT requires significant uplift across the breadth of the workforce.
  • HMT requires identifying the strengths and weaknesses of humans and machines and capitalising on advantages so that they become greater than the sum of their parts.
  • The focus of HMT should be on problems to be solved, the appropriate ratio and mode of HMI, and finding the sweet spot in terms of effort payoff.
  • RAS-AI must perform such that they instil a sense of trust, safety, and reliability in those who use them.
  • There must be proactive attention to the conceptual and ethical complexities of the HMT paradigm, starting from the moment of design.

Research conducted by

The research described in this report was prepared for the Royal Australian Navy, Robotics and Autonomous Systems/Artificial Intelligence Directorate and conducted by RAND Australia.

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