These new metrics help grade AI models' trustworthiness

Whether it's diagnosing patients or driving cars, we want to know whether we can trust a person before assigning them a sensitive task. In the human world, we have different ways to establish and measure trustworthiness. In artificial intelligence, the establishment of trust is still developing. In the past years, deep learning has proven to be remarkably good at difficult tasks in computer vision, natural language processing, and other fields that were previously off-limits for computers. But we also have ample proof that placing blind trust in AI algorithms is a recipe for disaster: self-driving cars that miss lane dividers, melanoma detectors that look for ruler marks instead of malignant skin patterns, and hiring algorithms that discriminate against women are just a few of the many incidents that have been reported in the past years.