Addressing The Cost Of Fairness In A Data Market Over Time

It is well understood that the data generation process is a critical factor that shapes the fairness of a machine-learning system.Since data generation is often mediated by a data market, we ask whether machine-learning fairness can be addressed in data markets as they evolve and, if so, at what cost.We revisit a well-known model of a data market in which data are allocated by a centralized marketplace.If the marketplace decides to enforce fairness, the main question is whether the natural extraction of value from data under a fairness intervention is further constrained and who is affected by it.In a natural class of allocation functions and under mild conditions, we show that no agent in the data market asymptotically loses utility as the market expands to include more buyers---even if the cost of data production is inherently biased against individuals of a particular group.Our initial results suggest that, under certain conditions, the evolution of a system may be a useful tool to address the cost of fairness.