A multidisciplinary survey on discrimination analysis

Most of the decisions in the today's knowledge society are taken
on the basis of historical data by extracting models, patterns,
profiles, and rules of human behavior in support of (automated)
decision making. There is then the need of developing models,
methods and technologies for modelling the processes of
discrimination analysis in order to discover and prevent
discrimination phenomena. In this respect, discrimination analysis
from data should build over the large body of existing legal and
economic studies. This paper intends to provide a
multi-disciplinary survey of the literature on discrimination data
analysis, including methods for data collection, empirical
studies, controlled experiments, statistical evidence, and their
legal requirements and grounds. We cover the following mainstream
research lines: labour economic models, (quasi-)experimental
approaches such as auditing and controlled experiments,
profiling-based approaches such as racial profiling and credit
markets, and the recently blooming research on knowledge discovery
approaches.