For both androids and chatbots, the uncanny valley problem might have something to do with how we perceive their capabilities. In a study published in Social Cognitive and Affective Neuroscience in 2012, researchers monitored people’s brain activity as they watched humans and robots move around in a video. They found that when participants watched a human moving fluidly and a very mechanical-looking robot moving jerkily, their brains reacted similarly. But when they watched a robot that looked human but didn’t act like one, their brain activity flared: They experienced what the researchers called a “prediction error.”
The same mechanic could be at play when humans interact with virtual assistants. When a bot is clearly a bot, the person interacting with it generally knows how limited its functions are. Take, for example, the digital assistant that answers when you call an airline’s 1-800 number: It asks you a few questions and tries to get you the information you’re looking for. If it can’t, it connects you with a human who can. The bot’s narrowly defined purpose guides the human that’s interacting with it.
By contrast, a smooth-talking virtual assistant that tries to mimic human speech, whether out loud or on a screen, can create different assumptions. “The more human-like a system acts, the broader the expectations that people may have for it,” said Justine Cassell, a computer-science professor at Carnegie Mellon University.
Cassell told me about a talking robot she and a colleague built in the ’90s that had a 2-D animated head on a computer screen. The head made certain human-like motions as it talked, like shifting its gaze and nodding at appropriate moments in the conversation. Cassell found that when users saw those cues, they unconsciously began talking to the computer more quickly and less clearly, the way they might talk to an actual person. That caused problems. “When people used it that way, they actually made it harder for the system to function effectively,” Cassell said.
Siri, of course, doesn’t have a face, and Facebook and Skype bots don’t even have a voice. But modern bots try to trick users into interacting with them as if they’re human in other ways: by bantering and using humor, speaking (or writing) conversationally, and learning to parse free-form questions and answers.
“This creates a perception that if you say anything to this bot, it should respond to it,” said Nikhil Mane, an engineer developing conversational AI at Autodesk. That sets the bot up for failure, Mane said. Without a sense of a bot’s limits, a user is liable to overstep the bounds of its ability. When the bot replies to an out-of-scope question in an odd or confusing way, it’s a dissonant reminder of its artificial nature.
Mane pointed to Slack, the popular workplace messaging app, for an example of a better approach. The app’s resident helper, Slackbot, prompts users to ask it “simple questions” about how Slack works. “I’m only a bot, but I’ll do my best to answer!” Slackbot says. “If I don’t understand, I’ll search the Help Center.”