Direct and Indirect Discrimination Prevention Methods - Discrimination and Privacy in the Information Society - page 233

Pay Act (United States Congress 1963) states that: “a selection rate for any race,
sex, or ethnic group which is less than four-fifths of the rate for the group with the
highest rate will generally be regarded as evidence of adverse impact”.
Discrimination can be either direct or indirect (also called systematic, see Pe-
dreschi et al. (2008)). Direct discriminatory rules indicate biased rules that are
directly inferred from discriminatory items ( e.g. Foreign worker = Yes). Indirect
discriminatory rules (redlining rules) indicate biased rules that are indirectly in-
ferred from non-discriminatory items ( e.g. Zip = 10451) because of their correla-
tion with discriminatory ones. Indirect discrimination could happen because of the
availability of some background knowledge (rules), for example, indicating that a
certain zipcode corresponds to a deteriorating area or an area with a mostly black
population. The background knowledge might be accessible from publicly availa-
ble data ( e.g. census data) or might be obtained from the original dataset itself
because of the existence of non-discriminatory attributes that are highly correlated
with the sensitive ones in the original dataset.
Fig. 13.1 The process of extracting biased and unbiased decision rules
One might conceive that, for direct discrimination prevention, removing discrimi-
natory attributes from the dataset and, for indirect discrimination prevention, re-
moving non-discriminatory attributes that are highly correlated with the sensitive
ones could be a basic way to handle discrimination. However, in practice this is
not advisable because in this process much useful information would be lost and
the quality/utility of the resulting training datasets and data mining models would
substantially decrease.
The rest of this chapter is as follows. Section 13.2 contains notation and back-
ground on direct and indirect discriminatory rules. Section 13.3 gives a taxonomy
of discrimination prevention methods. Section 13.4 describes several pre-
processing discrimination prevention methods we have proposed in recent papers.