Commentary: human-centred AI: the new zeitgeist

Hancock’s article on avoiding autonomous agent actions reads like a literary piece of work replete with esoteric phrasing and the odd bit of Latin thrown into the mix. It is cleverly crafted prose, with poignant metaphors warning us of a foreboding future of malfunctioning, dysfunctioning and failing autonomous machines that could wreak havoc in society and even destroy us if we don’t do something about them sharp. One of the most dramatic analogies is to cast autonomous systems like the evolution of a ring of volcanic islands rising from the ocean; abrupt and explosive rather than a slow gradual evolution. In contrast, we humans are viewed as taking the “littoral role of beaches and riparian shorelines,” receding as the volcanos spew forth. The oceanic imagery conjured up certainly does paint an apocalyptic future. I imagined hearing the soundtrack of War of the Worlds as I read it. So, what can and should we do about the predicted sudden eruption of autonomous machines before it is too late? Hancock suggests spending billions of dollars on training up a new cadre (sic) of machine forensic psychologists who would be able to see into the brains of our machines more clearly than we can now and understand better why they choose particular actions. All well and good, and a necessary call to arms. But this is already beginning to happen in the broader field of AI – albeit at not quite a grandiose scale. There is a growing body of Human-Computer Interaction (HCI) researchers, computer scientists, philosophers and psychologists addressing and confronting AI’s (sic) autonomy – working out how to replace it with alternative transparent algorithms that will allow humans (and other machines) to inspect, understand and put to right the machine learning and machine decisionmaking the algorithms are programmed to execute. Making these systems fairer, accountable, explainable and unbiased have become universally accepted goals. There are numerous frameworks, white papers and policies that have been published on how to achieve this. For example, in 2021 the EU published its Regulation that amongst its detailed pages of guidance, rules and restrictions, recommends banning AI systems that cause or are likely to cause “physical or psychological” harm to people (European Parliament.). These includes autonomous systems that recognize people’s faces and facial expressions without their knowledge; automatically deciding whether to permit or not them loans, credit, jobs and so on. Miller (2021) writes about Stanford’s pioneering HAI center in an article on the future of work, where she summarizes arguments for replacing AI-driven automation with alternative values that encourage a more human-centered workplace. What surprised me most about Hancock’s latest piece against autonomous tech – given it was submitted to the Journal of Human-Computer Interaction – was the almost complete lack of detailed technical discussion. What kinds of control, interactions and interfaces should we be considering and designing for technology, if we assume we don’t want it to be fully autonomous? There was one sentence midway through the piece that claimed we are “witnessing a crucial watershed in humanmachine interaction and teaming. ” But that was it. No real analysis about what human-machine interaction or teaming are. Instead, for most of the article, Hancock conducts a lengthy SWOT analysis about what would happen if we try our best to avert the impending catastrophe should the military and the arms industry be left to their own devices. But if Hancock had looked more closely at HCI research he