Can AI put humans back in the loop?
Is it possible to make artificial intelligence more trustworthy by inserting a human being into the decision process of machine learning?
It may be, but you don’t get something for nothing. That human being better be an individual who knows a lot about what the neural network is trying to figure out. And that presents a conundrum, given that one of the main promises of AI is precisely to find out things humans don’t know.
It’s a conundrum that is sidestepped in a new bit of AI work by scientists at the Technische Universität Darmstadt in Germany. Lead author Patrick Schramowski and colleagues propose to have a human check on the explanations provided by a neural network. The idea is to extend what’s been called “explainable AI” and “interpretable AI.” They contend that it’s not enough to have explanations of what a neural net is doing, the human should actually be intimately involved in fixing what goes wrong with a neural net.
In so doing, Schramowski and colleagues hope humans will gain greater trust in machine learning.
“[I]t is necessary to learn and explain interactively, for the user to understand and appropriately build trust in the model’s decisions,” write Schramowski and colleagues in Right for the Wrong Scientific Reasons: Revising Deep Networks by Interacting with their Explanations.
Their solution is “XIL,” standing for “explanatory interactive learning,” with the emphasis not only on providing explanations of machine behavior but also the exchange between person and machine.
The work is heavily inspired by the recent work of Sebastian Lapuschkin of the Fraunhofer Heinrich Hertz Institute in Berlin, who has written that neural networks can sometimes be like “Clever Hans.” Clever Hans was a famous horse who dazzled the public in the early 1900s by seeming to be able to do arithmetic.
Upon closer examination, it turned out Hans was merely responding to human gestures such as nods of the head. Lapuschkin argues despite the impressive quality of AI, sometimes it’s merely exploiting data set particularities rather than really learning relevant representations of a problem. People, therefore, need to have a bit of caution about all the “excitement about machine intelligence.”
Philosophically, the authors of the present work take a page from algorithm genius Don Knuth. They agree with Knuth that “Instead of imagining that our main task is to instruct a computer what to do,” the goal is to “let us concentrate rather on explaining to human beings what we want a computer to do.”
Schramowski and the team’s experimental setup for XIL is to have a convolutional neural network solve a straightforward problem in classifying the phenotype of a plant as healthy or diseased. They have the convolutional net examine images leaves of the sugar beet plant, a staple crop around the world, for instances of disease. They then visualize what features the network was using, then they have an expert on plant biology correct where the neural network fell down. Proper learning should involve the net focusing only on dark patches on the leaves of the plant that indicate the disease “Cercospora Leaf Spot.
