In the fall of 2016--the year in which the proportion of online adults using social media reached eighty per cent--I published an Op-Ed in the Times that questioned the popular conception that you need to cultivate a strong social-media brand to succeed in the job market. "I think this behavior is misguided," I wrote. "In a capitalist economy, the market rewards things that are rare and valuable. Social media use is decidedly not rare or valuable." I suggested that knowledge workers instead spend time developing useful skills, with the goal of distinguishing themselves in their chosen fields.

The fact that AI can pass on bias and prejudice is now widely recognized, probably because recent incidents of apparently racist or sexist algorithms involved big companies like Google and Amazon. A better understanding of how bad data gets encoded might make it easier to prevent. The large-scale machine learning AI that undergirds most recent advances relies on immense quantities of data. As the system feeds on the data provided, thousands of small adjustments are made to internal parameters to tweak how the data will be categorized. So, if the original training data is biased, the training is biased and the results will be biased.