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For some applications that's all you need.But it depends on the domain. “I largely agree.” Simple glassbox models can perform as well as more complicated neural networks on certain types of structured data, such as tables of statistics. Glassbox models are typically much-simplified versions of a neural network in which it is easier to track how different pieces of data affect the model.“There are people in the community who advocate for the use of glassbox models in any high-stakes setting,” says Jennifer Wortman Vaughan, a computer scientist at Microsoft Research.
Once the data has been cleaned up, a more accurate black-box model can be trained.It's a tricky balance, however. One solution is to take two passes at the data, training an imperfect glassbox model as a debugging step to uncover potential errors that you might want to correct. The ability of these networks to draw meaningful connections between very large numbers of disparate features is bound up with their complexity.Even here, glassbox machine learning could help.
Eleven AI professionals were recruited from within Microsoft, all different in education, job roles, and experience. But do they really help? In the first study of its kind, Vaughan and her team have tried to find out—and exposed some serious issues.The team took two popular interpretability tools that give an overview of a model via charts and data plots, highlighting things that the machine-learning model picked up on most in training. For example, the model could be relying too much on certain features, which could signal bias.These visualization tools have proved incredibly popular in the short time they’ve been around. The idea is that you can see serious problems at a glance. In a 2018 stud y looking at how non-expert users interact with machine-learning tools, Vaughan found that transparent models can actually make it harder to detect and correct the model’s mistakes.Another approach is to include visualizations that show a few key properties of the model and its underlying data.
And it instilled a false confidence about the tools that made participants more gung-ho about deploying the models, even when they felt something wasn’t quite right. This led to incorrect assumptions about the data set, the models, and the interpretability tools themselves. In some cases, users couldn’t even describe what the visualizations were showing. But this usefulness was overshadowed by a tendency to over-trust and misread the visualizations. Sure, the tools sometimes helped people spot missing values in the data. The experiment was designed specifically to mimic the way data scientists use interpretability tools in the kinds of tasks they face routinely.What the team found was striking.
“The automation bias was a very important factor that we had not considered.”Ah, the automation bias. “It was particularly surprising to see people justify oddities in the data by creating narratives that explained them,” says Harmanpreet Kaur at the University of Michigan, a coauthor on the study. They found similar confusion and misplaced confidence.Worse, many participants were happy to use the visualizations to make decisions about deploying the model despite admitting that they did not understand the math behind them.
But when this happens with tools designed to help us avoid this very phenomenon, we have an even bigger problem.What can we do about it? For some, part of the trouble with the first wave of XAI is that it is dominated by machine-learning researchers, most of whom are expert users of AI systems. When it comes to automated systems from aircraft autopilots to spell checkers, studies have shown that humans often accept the choices they make even when they are obviously wrong. It’s not a new phenomenon.
Now, when the neural network sees an action in the game, it “translates” it into an explanation. They then took a neural network for translating between two natural languages and adapted it to translate instead between actions in the game and natural-language rationales for those actions. Screenshot of Ehsan and Riedl's Frogger Explanation softwareTo do this, they showed the system many examples of humans playing the game while talking out loud about what they were doing. In an early prototype, the pair took a neural network that had learned how to play the classic 1980s video game Frogger and trained it to provide a reason every time it made a move. Ehsan and his colleague Mark Riedl are developing a machine-learning system that automatically generates such rationales in natural language. It is easier to understand what an automated system is doing—and see when it is making a mistake—if it gives reasons for its actions the way a human would.
If AlphaZero were able to explain its moves, would they always make sense?Reasons help whether we understand them or not, says Ehsan: “The goal of human-centered XAI is not just to make the user agree to what the AI is saying—it is also to provoke reflection.” Riedl recalls watching the livestream of the tournament match between DeepMind's AI and Korean Go champion Lee Sedol. One of the most striking features of the software is its ability to make winning moves that most human players would not think to try at that point in a game. Take DeepMind’s board-game-playing AI AlphaZero. For one thing, it is not clear whether a machine-learning system will always be able to provide a natural-language rationale for its actions.
(This is backed up by a new study from Howley and her colleagues, in which they show that people’s ability to understand an interactive or static visualization depends on their education levels.) Think of a cancer-diagnosing AI, says Ehsan. "But I felt that the commentary was essential to understanding what was happening."What this new wave of XAI researchers agree on is that if AI systems are to be used by more people, those people must be part of the design from the start—and different people need different kinds of explanations. "That wasn’t how AlphaGo worked," says Riedl.
“The more you say it’s smart, the more people are convinced that it’s smarter than they are. “We’ve always known that people over-trust technology, and that’s especially true with AI systems,” says Riedl.
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