This article argues that the fact that an action will compound a prior injustice counts as a reason against doing the action. I call this reason The Anti-compounding Injustice Principle or ACI. Compounding injustice and the ACI principle are likely to be relevant when analyzing the moral issues raised by “big data” and its combination with the computational power of machine learning and artificial intelligence.  

Past injustice can infect the data used in algorithmic decisions in two distinct ways. Sometimes prior injustice undermines the accuracy of the data itself. In these contexts, improving accuracy will also help to avoid compounding injustice. Other times, past injustice produces real world differences among people with regard to skills, health, wealth, and other traits that employers, lenders and others seek to measure. When decisions are based on accurate data that itself results from prior injustice, these decisions can also compound injustice. This second dynamic has received less attention than the first but is especially important because improving the accuracy of data will not mitigate this unfairness. 

Deborah Hellman, Big Data and Compounding Injustice, Journal of Moral Philosophy 1–22 (2023).