Towards human-machine teaming

The rising potential of AI, intertwines our lives with the use of AI-technology. It will increasingly behave as a partner rather than a tool. Yet AI-technology is always embedded within a larger organisation, where humans decide its purpose and framework. AI-technology does not act in isolation, therefore it must allow itself to be included in a larger network: a human-machine team.

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To ensure sustainable human-machine teaming, certain things are crucially important. Humans and AI-technologies must be able to mutually understand and anticipate one another’s context, needs, abilities and shortcomings. Currently, a proper level of mutual understanding and anticipation is lacking. TNO is advancing this mutual understanding by developing:

  • AI that learns to understand and interpret human abilities
  • AI that is capable of explaining its decisions and actions to humans

Tackling these research challenges will enable us to harness the full potential of AI as a partner in everyday human life.

Meeting the challenges posed by human-machine teaming

Human-machine teaming is strongly represented in the domains of health and mobility, safety and security. They are characterized by unpredictable or adverse conditions. A human-machine team must therefore be capable of reorganising itself efficiently. This should maximise the potential of the team. AI-technology can operate effectively and robustly, when the technology is socially capable, flexible, and aware of the larger context within which it operates.

The success of human-machine teaming depends on humans and AI-technology having a shared understanding. This relates to the team context, team member roles, responsibilities and resource needs. AI-technology should be capable of proactively inviting the human in the loop by sharing information about relevant developments. Furthermore, AI-technology should be capable of developing sustainable, dependable, and trustworthy relations with its team members. For example, by explaining its way of reasoning when suggesting a solution.

Achieving a shared understanding of roles and competencies is a gradual process. Relationships between team members develop over time as a result of experiences gained during training, exercises and operations. These are all occasions offering AI systems continuous input to learn from the dynamics with their team members and surroundings.

What does TNO offer?

  • We develop AI algorithms that provide meaningful explanations about the advice and decisions that they generate.
  • We develop frameworks, methods, and software for design and evaluation of human-machine teams.
  • We develop team design patterns that describe standardized and proven forms of collaboration between humans and intelligent autonomous agents in various contexts and problem domains.
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