Regulations on Three Big Data Discriminations Inducing Transaction Costs--From the Perspective of Legal Aid

Abstract

Individuals in transactions suffer from three types of big data discrimination: biased selection, false association, and malicious recommendation, which could induce additional transaction cost. Through algorithm model, enterprises process and derive the initial data of individuals. In this process, enterprises have added unfavorable algorithmic judgment conditions for individuals, restricting the individual's freedom of transaction and the right to choose, leading to unfairness in the transaction and increasing transaction cost for individuals. This article proposes that in order to suppress the adverse consequences of big data discrimination, data and algorithms should be included in regulatory objects at the same time, early warning mechanisms should be established, and supervising agencies should be stationed in data platforms by means of legal aid.