Let frontline workers guide AI processes
A recent article and analysis by Nick Reed at Brink News suggests the driverless car revolution isn’t gaining as much traction as originally predicted. “Five years ago, there was a lot of hype, a sense that this was something that was coming very soon and would be a part of the lives of many people,” he says. “But people underestimated the scale of the challenge of operating a fully automated, safety-critical system in an unpredictable and infinitely variable environment.”
The slow uptake of driverless cars may be an allegory for what may be a slower-than-expected uptake of artificial intelligence in general. After all, businesses surely operate in “unpredictable and infinitely variable environments” — and the past year has certainly put an exclamation point on this observation.
Success with AI is going to be a bumpy and uneven road, and is going to need more human knowledge and input than we can ever imagine. At its core, artificial intelligence isn’t really a job-killer — it’s more of a taskmaster. The challenge for people and organizations, then, is to provide the knowledge and training to prepare for this new age. No one is more critical to AI success than the people who will be working with it on the front lines.
That’s the word coming out of a recent panel discussion, hosted by Stanford University’s center for Human-Centered Artificial Intelligence (HAI). More needs to be done to engage the workforce to help steer the changes AI is bringing about in a positive direction. Business leaders and policymakers need to be more proactive in this process.
While jobs will be lost, many more will be enhanced, elevated, and created, says James Manyika, chairman of the McKinsey Global Institute. “There will be jobs that’ll be lost, partly because technology will be able to do the various activities involved in that job. There will be jobs that will be changed. The jobs change is part of the fact that while the job is still there, technology will complement some of those activities.”
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The share of jobs and occupations “that can be fully automated in terms of all their constituent activities is actually relatively small, at least for the next several decades,” Manyika adds.
How should we prepare the workforce for the changes ahead — to encourage their involvement in the AI revolution, as well as deliver the skills needed to make it happen? Many jobs, for example, are frontline workers who don’t have access to many technology tools. This is a crucial question, says Mary Kay Henry, international president of the Service Employees International Union (SEIU). “Seventy percent of the U.S. workforce is in the service sector, and one in four jobs are care jobs. Those jobs are poverty jobs that, in most cases, there’s two million black, brown, and immigrant women doing home care work in this nation. It’s the fastest growing job [category]. I think those workers would love to understand how technology and artificial intelligence could actually elevate the work they do caring for the nation’s elderly and people with disabilities.”
There are structural barriers to this kind of work that do not lend it too well to AI and automation retraining, Henry points out. “There’s a lack of regular schedule. They have to hustle for hours every week, and one week to the next, they don’t know really what their schedule is, which makes it impossible for them to engage in any kind of ongoing education. That also keeps them from being able to have a dialogue with their employers about how artificial intelligence in technology gets introduced into their work.”
Empowering the jobs of frontline workers is “where technology that complements what workers do, what people do, becomes critically important,” Manyika responds, advocating that workers be fully involved in the technology adoption process. “What I always tell people when we have this conversation is don’t worry about a jobless future. It is not for many, many decades. What we should think about is how we manage the transitions and adaptations as we help workers cope with this.”
For starters, he continues, “we do have to solve the skills question because as jobs change, we’re going to need to make sure that workers can actually adapt, learn skills, be able to work alongside machines, or move into occupations that are actually growing. The second question is how do we help workers transition either form declining occupations to the occupations that are growing? This is where policy and other mechanisms are really, really important to make sure we support the workers, we have the safety nets and the benefit models, and transition supports to actually help workers transition.”
This is critical, as a large segment of the workforce now, and going forward, are occupations such as care workers and teachers, who need to be compensated more fairly. Finally, Manyika says, “we need to solve for is how do we actually redesign work, because what happens is, the workplace actually changes as we bring in technology to the workforce. How do we think about data in the workplace? How do we think about redesigning the work itself? By the way, if we didn’t think these questions about redesigning work were urgent, we only have to pay attention to what’s happened with Covid.”
There is growing recognition that AI and robotics will only succeed when there is a human component to processes, he adds. “There are certain aspects of training where it actually is very helpful to actually do that with workers alongside the machines. How do you think about robotic manipulation, for example? Some of that training can actually be done by the machines. One of the things that’s interesting in this Covid moment is robotic mechanisms have actually been quite important in actually doing things like improving testing. Quite often, when machines are trying to learn how to place and locate things in a space and in an environment, it’s often very helpful to learn that from real human beings, so work alongside human beings… There’s a co-learning aspect to this. I think that’s another way in which workers can be part of the process.”
Healthcare provides an exemplary proving ground for the ways people can work to design AI-driven workplaces and processes that deliver the benefits intended. “In healthcare, we recognize that looping in these workers, from clinicians to caretakers, we start to understand deeply the privacy concerns and ethical concerns,” says Fei-Fei Li, co-director of Stanford HAI. “When we started working with the nurses in Stanford Hospital about hand-hygiene practice, the initial reaction was ‘You’ll never be allowed to do this because there is a privacy infringement, or actually concern for the nurses.’ But as we loop in the nurses and understand the concerns, we share the technology that does not infringe on privacy, you realize these people are so incredible. They are creative, they’re supportive, they want what’s best for the patients, they become our biggest allies and partners in this. There is a lack of culture in tech right now to involve more of our workers, especially service workers, in the early stage of the tech design, all the way to the application stage.”