A.I. Needs New Clichés
By: Molly Wright Steenson
Tags: Cliches, AI, Design, Artificial Intelligence

They thought we’d be there in 1978. Forty years later, we are there.

Or there’s this vision. Theodore Twombly (Joaquin Phoenix) moves through a rose-tinted world in the movie Her. By day, he works for a company that writes letters for people who can’t emotionally muster it — himself an A.I. for people’s emotional worlds. He is dating Samantha, his intelligent operating system. Theodore moves through an urban world, having deeply connected conversations with Samantha yet remaining disconnected from the people around him. He is attuned to a voice that only he can hear, but who can be the same voice for thousands of others at the same time. Is he affected by the strange virtuality of his love, or avoiding the difficulties of relating to a real person, or both? The movie world of Her is color-corrected like gauzy Instagram photos of Coachella fans, an interface of image in which nothing is not a computer.

How do you communicate what you don’t understand?

The problem is that it’s hard to communicate clearly about A.I. — in part because communicating about it means understanding it. And most of us don’t have a clear understanding of what A.I. actually is. The term “artificial intelligence” has been around since 1955, when A.I. pioneer John McCarthy wrote that A.I. was a matter of “making machines do things that would require intelligence if done by man.” That idea hasn’t changed much today — Wikipedia contrasts artificial intelligence (or machine intelligence) with the natural intelligence of humans and animals, and the Oxford English Dictionary defines it as “the capacity of computers or other machines to exhibit or simulate intelligent behaviour; the field of study concerned with this. Abbreviated A.I.” But what does that actually mean to an everyday person? Does it mean chatbots passing a Turing Test? A robot uprising? Or is it simply interactivity where the processing happens just out of sight?

A.I. is a black box, “a device which performs intricate functions but whose internal mechanism may not readily be inspected or understood” (OED)—something we understand because of the inputs and the outputs. We can’t see what happens inside and we’re not meant to have access to it. The black box is opaque.

Today, there are three reasons for algorithmic opacity, as Jenna Burrell writes: the need to protect algorithms that are state or corporate secrets; the fact that A.I.-related coding is still the territory of specialists; and a “mismatch” between the mathematical ways algorithms process information and the way humans think. This last one is the hardest to sort out: how humans think is different than how the machine thinks, and how we reason is different than how the machine reasons (or doesn’t reason, depending on your definition of “reason”).

The European Union is requiring that A.I. be made explainable through a “right to explanation” as part of the GDPR (General Data Protection Regulation) that just took effect. EU citizens have a right to an explanation for the work of algorithms, and they have a right to request human intervention. The regulations support the idea that “people are owed agency and understanding when they’re faced by machine-made decisions,” Cliff Kuang wrote in the New York Times. David Weinberger argues that rather than explaining itself, one focus of A.I. should be optimization, not explanation: make visible and clear to all through debate in public policy what trade-offs are being made, rather than potentially hobbling A.I.

The US government has never been known for well-designed PowerPoint decks

In the meantime, DARPA has introduced the Explainable Artificial Intelligence program, which seeks to make the models behind machine learning and A.I. more explainable. It’s an important move toward understanding what we mean when we talk about A.I., and yet I wonder what it will actually achieve for everyday people. The DARPA page for the project serves up this image showing what a user might be asking, but not in any quick way that’s going affect how it shows up in the world. Perhaps not surprisingly, DARPA returns to the same clichés as the Google Image Searches I mentioned above.