Abstract

In the early years of the “big data revolution,” big data was described by 4 or 5 v’s (or more) - volume, velocity, variety, veracity, and value. Although intended to describe data, the 5 v’s can provide insights into what makes ethical decision making hard. The volume of work, the expectation of speed, the variety of problems, the veracity or maybe more explicitly the provenance of the data, and the value of the work viewed from the diverse and sometimes competing perspectives of stakeholders can make ethically navigating the data science landscape challenging. As the field grows, the need for resources and tools has become more urgent. In this article, we will briefly examine the history of several ethical guidelines and frameworks.

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