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How important is explainability? Applying clinical trial principles to AI safety testing
Explainability is a heightened focus in AI. How existing scientific techniques can ensure AI is working as intended — and safely.
Similarly, Google Deepmind’s cancer screening models use deep learning methods to build accurate disease detection that can save lives. This framework allowed technology companies like Bing — and later Uber, Airbnb and many others — to make iterative changes to their products and user experience and understand the benefit of these changes on key business metrics. The result is that many companies now have a system to iteratively test changes to a technology against a control or a benchmark: One that can be adapted to measure not just business benefits like clickthrough, sales and revenue, but also causally identify harms like disparate impact and discrimination.
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