Too much transparency can lead to information overload. Once the data has been cleaned up, a more accurate black-box model can be trained. 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.Д®ven here, glassbox machine learning could help.

If we want to learn from messy data like images or text, weвЂ™re stuck with deep-and thus opaque-neural networks. For some applications that's all you need.Д«ut it depends on the domain. вЂњI largely agree.вЂќ Simple glassbox models can perform as well as more complicated neural networks on certain types of structured data, such as tables of statistics. вЂњThere are people in the community who advocate for the use of glassbox models in any high-stakes setting,вЂќ says Jennifer Wortman Vaughan, a computer scientist at Microsoft Research. 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. One solution is to build machine-learning systems that show their workings: so-called glassbox-as opposed to black-box-AI. The aim of so-called interpretable or explainable AI (XAI) is to help people understand what features in the data a neural network is actually learning-and thus whether the resulting model is accurate and unbiased. вЂњYou can see this playing out right now with the public response to facial recognition systems,вЂќ she says.Д®hsan is part of a small but growing group of researchers trying to make AIs better at explaining themselves, to help us look inside the black box. Without this, people will push back against the technology. People need the power to disagree with or reject an automated decision, says Iris Howley, a computer scientist at Williams College in Williamstown, Massachusetts. But if AI is to be used to help make decisions in law enforcement, medical diagnosis, and driverless cars, then we need to understand how it reaches those decisions-and know when they are wrong. We have come to think of them as black boxes.ДЄ lot of the time weвЂ™re okay with that when it comes to things like playing Go or translating text or picking the next Netflix show to binge on. As a result, the details of how a trained model works are typically unknown. The success of deep learning is due to tinkering: the best neural networks are tweaked and adapted to make better ones, and practical results have outpaced theoretical understanding. It got Ehsan thinking: what if the self-driving car could really explain itself? Instead of fretting about the empty driverвЂ™s seat, anxious passengers were encouraged to watch a вЂњpacifierвЂќ screen that showed a carвЂ™s-eye view of the road: hazards picked out in orange and red, safe zones in cool blue.ДЇor Ehsan, who studies the way humans interact with AI at the Georgia Institute of Technology in Atlanta, the intended message was clear: вЂњDonвЂ™t get freaked out-this is why the car is doing what itвЂ™s doing.вЂќ But something about the alien-looking street scene highlighted the strangeness of the experience rather than reassured. Upol Ehsan once took a test ride in an Uber self-driving car.

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