The increasing popularity of online two-sided markets such as ride-sharing, accommodation and freelance labor platforms, goes hand in hand with new socioeconomic challenges. One major issue remains the existence of bias and discrimination against certain social groups.

The increasing popularity of online two-sided markets such as ride-sharing, accommodation and freelance labor platforms, goes hand in hand with new socioeconomic challenges. One major issue remains the existence of bias and discrimination against certain social groups.

We analyze statistical discrimination using a multi-armed bandit model where myopic firms face candidate workers arriving with heterogeneous observable characteristics. The association between the worker's skill and characteristics is unknown ex ante; thus, firms need to learn it. In such an environment, laissez-faire may result in a highly unfair and inefficient outcome---myopic firms are reluctant to hire minority workers because the lack of data about minority workers prevents accurate estimation of their performance. Consequently, minority groups could be perpetually underestimated---they are never hired, and therefore, data about them is never accumulated. We proved that this problem becomes more serious when the population ratio is imbalanced, as is the case in many extant discrimination problems. We consider two affirmative-action policies for solving this dilemma: One is a subsidy rule that is based on the popular upper confidence bound algorithm, and another is the Rooney Rule, which requires firms to interview at least one minority worker for each hiring opportunity. Our results indicate temporary affirmative actions are effective for statistical discrimination caused by data insufficiency.

The use of robots in the workplace has more than double in just a 12 year period, displacing 50 percent of many human workers across the US, studies have found. A new interactive map provides more detail into this'robot exposure' by highlighting the top 10 metropolitan areas threatened by this machine takeover – California being listed as number one. In addition to areas most at risk, experts found that automation is displacing younger, less-educated and minority workers at the highest rates. The study and map were developed by The Century Foundation, a progressive think tank headquartered in New York City, which looked across more than 250 metropolitan areas to understand this'robot intensity'. Los Angeles, Long Beach and Santa Ana, California were ranked number one, followed by Chicago, Naperville and Joliet in Illinois.