Unlock AI’s full potential through human-machine collaboration
September 16, 2020
Agency leaders should evaluate AI’s potential in two ways—opportunities where AI can work independently to streamline operations and where it can be used collaboratively with human workers to improve their performance.
For the former, AI can play a role in preprocessing or prioritizing critical information to reduce the administrative burden. For example, the Social Security Administration is using AI to identify medical documentation that is most useful in supporting a disability claim. In many cases, agencies will need to rethink legacy process flow with AI used earlier to minimize bottlenecks and shift workers to more complex analysis or roles requiring empathy.
For the latter, it’s about considering how AI can aid the worker’s decision making and analysis. A good example is the role that AI is playing in many call centers. In addition to prepopulating forms, it can monitor a conversation to coach and recommend potential solutions to a call center representative. It’s not hard to imagine similar approaches being widely applied in fields like medicine and law enforcement.
By taking these steps, federal leaders can unlock the full potential of their workforce to operate with greater agility and effectiveness to achieve better outcomes.
So how can federal agencies do more to bring out the full power of their people? They can start by moving beyond deploying AI for automation alone and push into the new frontier of co-creation between people and machines. Natural language processing (NLP), explainable AI, and extended reality (XR) are among the tools that can unlock new ways for humans to interact with machines and for machines to interact with us.
01 Natural language processing
Meaningful collaboration always begins with communication, and traditional language barriers between humans and machines are disappearing for both written and spoken text through advancements in NLP. In many cases, these advances are pushing AI outside the realm of data scientists alone and into the core operations of the organization, giving average users the ability to command powerful systems that were unimaginable just a few years ago.
By leveraging these advances, businesses can deepen human-AI collaboration. Google’s BERT and Baidu’s ERNIE—which are both open-source frameworks—enabled AI systems to move from understanding just one word to understanding phrases in context.
02 Explainable AI
Collaboration can’t just be one-way—organizations must complete the feedback loop and build capabilities that allow humans to better understand machines. The growing field of explainable AI is letting humans de-mystify the output of previously “black-box” AI systems—making human-machine collaboration possible even if the AI wasn’t designed to explain its decision-making process, through approaches like counterfactual explanations. If a citizen is denied a loan or benefit, for example, the system needs to be able to explain the reasons for the denial and offer the smallest number of changes the applicant would need to make to have the application approved. Making AI explainable turns a human-AI interaction into a relationship.
03 Extended reality
Likewise, machines can be valuable collaborators when they can understand the physical context of humans and can sense—and make sense of—a person’s surroundings. For example, image recognition and machine learning allow Microsoft’s HoloLens 2 mixed reality headset to not only see, but also understand the wearer’s physical environment. This contextual understanding of the environment unlocks new capabilities for the device, like being able to identify dangerous equipment and warn the wearer if the equipment is operating hazardously. This significance will grow dramatically with the deployment of 5G and adoption of edge computing models.
On the Exploring AI in Government podcast series, Dr. Tim Persons, GAO’s chief scientist, shared “I think we're still underestimating how much we're going to get out of [AI] over time as it evolves. I think it's going to surprise us . . . we're going to look back and say, I can't believe we used to do things that way.” Federal leaders will need to approach AI with a similar sense of both wonder and ambition to realize its full potential.