Six steps companies should take before adopting a 'black-box' approach.
to predict likelihood of defaulting on a loan that was simple enough that average banking customers could easily understand it, and the researchers found that their model was less than 1% less accurate than an equivalent black box model .
Of course, there are some cases in which black-box models are still beneficial. But in light of the downsides, our research suggests several steps companies should take before adopting a black-box approach:As a rule of thumb, white-box models should be used as benchmarks to assess whether black-box models are necessary. Before choosing a type of model, organizations should test both — and if the difference in performance is insignificant, the white-box option should be selected.
Second, the type of data also affects the decision. For applications that involve multimedia data such as images, audio, and video, black-box models may offer superior performance. For instance, we worked with a company that was developing AI models to help airport staff predict security risk based on images of air cargo. They found that black-box models had a higher chance of detecting high-risk cargo items that could pose a security threat than equivalent white-box models did.
For instance, in domains such as hiring, allocation of organs for transplant, and legal decisions, opting for a simple, rule-based, white-box AI system will reduce risk to both the organization and its users. Many leaders have discovered these risks the hard way: In 2015, Amazon
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