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When creating a machine-learning algorithm, designers have to make many choices: what data to train it on, what specific questions to ask, how to use predictions that the algorithm produces. These choices leave room for discrimination, particularly against people who have been discriminated against in the past. For example, training an algorithm to select potential medical students on a data set that reflects longtime biases against women and people of color may make these groups less likely to be admitted. In computing, the phrase "garbage in, garbage out" describes how poor-quality input leads to poor-quality output. In this case we might say, "White male doctors in, white male doctors out."

Many public conversations we have about science-related issues involve communicating risks: describing them, comparing them and trying to inspire action to avoid or mitigate them. Just think about the ongoing stream of news and commentary on health, alternative energy, food security and climate change. Good risk communication points out where we are doing hazardous things. It helps us better navigate crises. It also allows us to pre-empt and avoid danger and destruction. But poor risk communication does the opposite. It creates confusion, helplessness and, worst of all, pushes us to actively work against each other even when it's against our best interests to do so. So what's happening when risk communications go wrong?