For many years, interacting with artificial intelligence has been the stuff of science fiction and academic projects, but as smart systems take over more and more responsibilities, replace jobs, and become involved with complex emotionally charged decisions, figuring out how to collaborate with these systems has become a pragmatic problem that needs pragmatic solutions. Machine learning and cognitive systems are now a major part many products people interact with every day, but to fully exploit the potential of artificial intelligence, people need much richer ways of communicating with the systems they use. The role of designers is to figure out how to build collaborative relationships between people and machines that help smart systems enhance human creativity and agency rather than simply replacing them.
Machine learning and cognitive systems are now a major part many products people interact with every day, but to fully exploit the potential of artificial intelligence, people need much richer ways of communicating with the systems they use. The role of designers is to figure out how to build collaborative relationships between people and machines that help smart systems enhance human creativity and agency rather than simply replacing them.
Imagine you are commuting in an autonomous car when it suddenly slams on the brakes, changes course, and heads off in a new direction. Maybe the car saw something you didn’t or found out about an accident ahead, but if it doesn’t communicate all this to you and you don’t trust it to make a snap decision, a change in course without any indication of why will be extremely disturbing. For the most part, cars don’t face morally challenging decisions, but sometimes they do, such as which way to swerve in a crowded accident situation, so before self-driving cars can really take off, people will probably have to trust their cars to make complex, sometimes moral, decisions on their behalf, much like when another person is driving. Areas like health care are even more fraught, and AI is getting involved there, too.
Creating a feedback loop In a conversation, I may misunderstand what you ask me, or I may need more information, but either way, the back and forth nature of the communication allows you to quickly correct my errors and lets me fill in any gaps in what I need to know. A similar human to machine interaction allows the system to get the information it needs to understand the questions, even when the information necessary for understanding the problem can’t be defined ahead of time. This also takes advantage of one of the key distinguishing capabilities of many AI systems: they know when they don’t understand something.
Once a system gains this sort of self-awareness, a fundamentally different kind interaction is possible. One of the biggest challenges of interface design is figuring out what information is appropriate in a given context so that the rest can be removed or de-emphasized. What happens when the system itself can make these kind of judgments?
Many people are warning about the potential for AI-driven automation to destroy the economy by eliminating most jobs. To take the turn of the last century as an example, the widespread introduction of the car had a huge positive impact on most people’s lives, but it also put almost all horses out of work. At the beginning of the 21st century, are we the drivers who will benefit from today’s technical revolution, or are we the horses hauling construction materials to Henry Ford’s new factory? Developing AI systems that work collaboratively with people rather than simply replacing them can help ensure that the benefits of AI are spread among more people, creating systems that are smarter than either people or machines alone.
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