Opening up DOD's AI black box

As algorithms and other AI tools become more prominent to military operations, researchers are studying better ways to visualize and communicate their decision-making.

machine learning
 

The Department of Defense is racing to test and adopt artificial intelligence and machine learning solutions to help sift and synthesize massive amounts of data that can be leveraged by their human analysts and commanders in the field. Along the way, it's identifying many of the friction points between man and machine that will govern how decisions are made in modern war.

The Machine Assisted Rapid Repository System (MARS) was developed to replace and enhance the foundational military intelligence that underpins most of the department's operations. Like U.S. intelligence agencies, officials at the Pentagon have realized that data -- and the ability to speedily process, analyze and share it among components – was the future. Fulfilling that vision would take a refresh.

"The technology had gotten long in the tooth," Terry Busch, a division chief at the Defense Intelligence Agency, said during an Apr. 27 virtual event hosted by Government Executive Media. "[It was] somewhat brittle and had been around for several decades, and we saw this coming AI mission, so we knew we needed to rephrase the technology."

In February, DOD formally adopted its first set of principles to guide ethical decision-making around the use of AI. The 80-page document was the product of 15 months of study by the Defense Innovation Board, and defense leaders have pledged not to use tools that don't abide by the guidance as they seek to push back on criticism from Silicon Valley and other researchers who have been reluctant to lend their expertise to the military.

The broader shift from manual and human-based decision-making to automated, machine-led analysis presents new challenges. For example, analysts are used to discussing their conclusions in terms of confidence-levels, something that can be more difficult for algorithms to communicate. The more complex the algorithm and data sources it draws from, the trickier it can be to unlock the black box behind its decisions.

"When data is fused from multiple or dozens of sources and completely automated, how does the user experience change? How do they experience confidence and how do they learn to trust machine-based confidence?" Busch said, detailing some of the questions DOD has been grappling with.

The Pentagon has experimented with new visualization capabilities to track and present the different sources and algorithms that were used to arrive at a particular conclusion. DOD officials have also pitted man against machine, asking dueling groups of human and AI analysts to identify an object's location – like a ship – and then steadily peeling away the sources of information those groups were relying on to see how it impacts their findings and the confidence in those assertions. Such experiments can help determine the risk versus reward of deploying automated analysis in different mission areas.

Like other organizations that leverage such algorithms, the military has learned that many of its AI programs perform better when they're narrowly scoped to a specific function and worse when those capabilities are scaled up to serve more general purposes.

Nand Mulchandani, chief technology officer for the Joint Artificial Intelligence Center at DOD, said the paradox of most AI solutions in government is that they require very specific goals and capabilities in order to receive funding and approval, but that hyper-specificity usually ends up being the main obstacle to more general applications later on. It's one of the reasons DOD created the center in the first place, and Mulchandani likens his role to that of a venture capitalist on the hunt for the next killer app.

"Any of the actions or things we build at the JAIC we try to build them with leverage in mind," Mulchandani said at the same event. "How do we actually take a pattern we're finding out there, build a product to satisfy that and package it in a way that can be adopted very quickly and widely?"

Scalability is an enduring problem for many AI products that are designed for one purpose and then later expanded to others. Despite a growing number of promising use cases, the U.S. government still is far from achieving desired end state for the technology. The Trump administration's latest budget calls for increasing JAIC's funding from $242 million to $290 million and requests a similar $50 million bump for the Defense Advanced Research Projects Agency's research and development efforts around AI.

Ramping up the technology while finding the appropriate balance in human/machine decision-making will require additional advances in ethics, testing and evaluation, training, education, products and user interface, Mulchandani said.

"Dealing with AI is a completely different beast in terms of even decision support, let alone automation and other things that come later," he said. "Even in those situations if you give somebody a 59% probability of something happening …instead of a green or red light, that alone is a huge, huge issue in terms of adoption and being able to understand it."

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