An AI That’s Not Artificial at All

Messy and unpredictable situations—such as coordinating medical care during military conflicts—have exposed the limits of current design practices. Could a new methodology empower humans to innovate and solve problems?

Despite the fact that geopolitical tensions had been steadily increasing over the last week and the entire base was on high alert, the shock and violence of the missile attack on the airfield at 0230 was overwhelming. Trauma surgeon Colonel William Smith hastily entered the hospital and scrubbed in to prepare for the wave of casualties.

In 2017, the Defense Advanced Research Projects Agency (DARPA) ran a series of wargames with military logisticians, medical professionals, aviators, and planners. Building on a scenario like the one above, the goal of the wargames was to imagine new ways to evacuate and treat the wounded in future conflicts. As DARPA program managers, we worked through the profound logistical challenges of saving wounded service members across multiple military services and host nations. This experience led us to develop a new way of thinking about how to use artificial intelligence to help people work together in complex, multi-system, difficult-to-predict situations. Instead of focusing on what humans or machines are better at, we focused on the liminal spaces—gaps between systems and individuals, for example, or between the present and the future—with the aim of sewing together intelligences to empower humans to innovate and solve problems.

Amid a global pandemic that has revealed our inability to quickly bridge crucial gaps in knowledge and systems, this approach, which we call liminal design, seems particularly promising for its ability to mediate interactions between people and coordinate their efforts around common goals. Virtual work, networks, and technology platforms have overturned long-held assumptions about how humans (and machines) can work together, especially in the last year and a half. Technology built with AI may soon be used to address emergent challenges such as fighting wildfires with input from interdisciplinary teams of firefighters, forest ecologists, and meteorologists, or, similarly, coordinating multi-institutional responses to public health or military emergencies. For all of their promise, however, these technologies and configurations challenge fundamental assumptions about decision making, autonomy, and scale in how we organize ourselves. As liminal design and other methods are developed to help orchestrate human-machine collaboration, their implications for governance will need close examination.

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