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 study this problem using a two-sided large market model with employers and workers mediated by a platform. Employers who seek to hire workers face uncertainty about a candidate worker's skill level. Therefore, they base their hiring decision on learning from past reviews about an individual worker as well as on their (possibly misspecified) prior beliefs about the ability level of the social group the worker belongs to. Drawing upon the social learning literature with bounded rationality and limited information, uncertainty combined with social bias leads to unequal hiring opportunities between workers of different social groups. Although the effect of social bias decreases as the number of reviews increases (consistent with empirical findings), minority workers still receive lower expected payoffs. Finally, we consider a simple directed matching policy (DM), which combines learning and matching to make better matching decisions for minority workers. Under this policy, there exists a steady-state equilibrium, in which DM reduces the discrimination gap.

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 study this problem using a two-sided large market model with employers and workers mediated by a platform. Employers who seek to hire workers face uncertainty about a candidate worker's skill level. Therefore, they base their hiring decision on learning from past reviews about an individual worker as well as on their (possibly misspecified) prior beliefs about the ability level of the social group the worker belongs to. Drawing upon the social learning literature with bounded rationality and limited information, uncertainty combined with social bias leads to unequal hiring opportunities between workers of different social groups.

We simulate a reputation system in a market to optimise the balance between market security and market equity. We introduce a method of using a reputation system that will stabilise the distribution of wealth in a market in a fair manner. We also introduce metrics of a modified Gini that takes production quality into account, a way to use a weighted Pearson as a tool to optimise balance.

It may seem obvious why the online market has such high growth rates, especially considering the events that occurred in the last couple of years and how they pushed people to use the internet for many services, including retail and banking. Similarly, daily and weekly bonuses and promotions have increased registration numbers on online casino sites. So, attributes such as electronic payment options, daily bonuses, and promotions all encourage new and existing players to keep coming back for more. The application of AI in gathering user data on new and returning players has assisted operators and developers in keeping fresh content that maintains relevance while creating targeted marketing campaigns. The bad news for those employees is that they would end up losing work to robots, which means an entire industry would take a hit. It may seem obvious why the online market has such high growth rates, especially considering the events that occurred in the last couple of years and how they pushed people to use the internet for many services, including retail and banking.

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 study this problem using a two-sided large market model with employers and workers mediated by a platform. Employers who seek to hire workers face uncertainty about a candidate worker's skill level. Therefore, they base their hiring decision on learning from past reviews about an individual worker as well as on their (possibly misspecified) prior beliefs about the ability level of the social group the worker belongs to.