Why Unbiased Computational Processes Can Lead to Discriminative Decision Procedures - Discrimination and Privacy in the Information Society - page 52

be obtained. 1 An important side effect of this treatment, however, will be that a
calm male driver will actually receive a higher insurance premium than an aggres-
sive female driving the same car and being of the same age. The statistical discrim-
ination theory (see Fang and Moro, 2010) states that inequality may exist between
demographic groups even when economic agents (consumers, workers, employers)
are rational and non-prejudiced, as stereotypes may be based on the discriminated
group's average behavior. 2 Even if that is rational, according to anti-discrimination
laws, this may constitute an act of discrimination, as the male person is discrimi-
nated on the basis of a characteristic that pertains to males as a group, but not to
that person individually. Of course, a classifier will have to base its decisions upon
some characteristics, and the incompleteness of the data will inevitably lead to sim-
ilar phenomena; e.g., an exaggerated importance in the decision procedure on the
color of the car, the horsepower, the city the person lives in, etc. The key issue
here, however, is that some attributes are considered by law to be inappropriate to
generalize upon, such as gender, age, religion, etc., but others, such as horsepower
or a color of a car are not.

3.4 Potential Solutions for Discrimination Free Computation

We argued that unbiased computational processes may lead to discriminatory de-
cisions due to historical data being incorrect or incomplete. In this section we dis-
cuss the main principles how to organize computational modeling in such a way
that discrimination in decision making is prevented. In addition, we outline the
main challenges and problems to be solved for such modeling.

3.4.1 Basic Techniques That Do Not Solve the Problem

We start with discussing the limitations of several basic solutions for training
computational models.

Removing the Sensitive Attribute

Table 2 Example (fictitious) dataset on lending decisions

Customer no. Ethnicity Work exp. Postal code Loan decision

#1 European 12 years 1212 +

#2 Asian 2 years 1010 -

#3 European 5 years 1221 +

#4 Asian 10 years 1011 -

#5 European 10 years 1200 +

#6 Asian 5 years 1001 -

#7 European 12 years 1212 +

#8 Asian 2 years 1010 -

1 Wikipedia: Proxy (statistics).
2 Wikipedia: Statistical discrimination (economics).