Big Data

Our current age is often referred to as the era of big data. While the term has become increasingly popular in the last 10 years, the concept is now used in a wide variety of ways, often without referring to an acknowledged technical definition of the concept. In fact, an agreed-upon and technical definition can be argued not to exist, and the term big data is mainly used to refer to broad and general tendencies related to a rapid increase in the data collected in various domains and the accompanying need to develop and employ new methods of analysis in order to make sense of such data. These methods of analysis connect big data intimately to data mining, artificial intelligence (AI), and machine learning.

Big data has become a ubiquitous phenomenon, and it is applied to domains such as research, business intelligence, social networks, health care, crime prevention, and anti-terrorism. Data is routinely described as the “new oil,” and in parallel with the historical discovery of other precious [Page 60]resources, the quest to drill, mine, and refine it is both intense and characterized by competition, rapid development, and private sector innovation. Big data carries a number of actual and potential benefits, but it is also related to a number of ethical challenges. Many are related to the nature of the data employed, some relate to a purported blind and dangerous faith in quantitative data and algorithms as the solution to and source of insight in all aspects of human lives, and some are mainly related to the use of algorithmic decision-making in general. The next section of this entry presents an expanded definition of big data, followed by an exploration of the philosophical basis of the concept and its roots in the quantitative and empiricist approach to knowledge. Before concluding, the entry offers a discussion of the ethical considerations surrounding the growing applications of big data.