Max Simkoff, [big data analysis company] Evolv’s co-founder and CEO, told me that his company’s big-data crunching had revealed a stream of intriguing, contrarian results. For example, “people with a criminal background stay longer on the job and perform better at entry-level hourly jobs,” he said. Having “relevant experience” for a job didn’t track with later productivity. Indeed, the relative quality of a manager or supervisor was more important in influencing worker attrition and productivity than the background of the individual workers. Other useful insights — as reported by the Atlantic’s Don Peck in a comprehensive recent feature story, “They’re Watching You At Work” – include the nugget that educational attainment is not as big a factor in job success as the conventional wisdom believes. Another interesting data point: Being unemployed for a long period of time does not make you a worse worker, if hired. Put it all together, says Simkoff, and you end up with a better world: Listening to the wisdom of the algorithm, he believes, results in a fairer workplace, less tainted by bias and discrimination.
Leonard considers how the “wisdom of the algorithm” might not translate to all workplaces:
[T]here’s a darker scenario, one that increasingly seems to be playing out already: The best workers reap huge rewards; everyone else struggles for the scraps. Because that’s the logic of the algorithm. Reward productivity and punish inefficiency. It’s a great model for an NBA team, with only 11 or 12 spots on the roster. But it’s not all clear that it’s a great way to run an entire society.
Earlier Dish on people analytics here.