Detecting privacy and ethical sensitivity in data mining results
Abstract
Knowledge discovery allows considerable insight into
data. This brings with it the inherent risk that what
is inferred may be private or ethically sensitive. The
process of generating rules through a mining operation
becomes an ethical issue when the results are
used in decision making processes that affect people,
or when mining customer data unwittingly compromises
the privacy of those customers.
Significantly, the sensitivity of a rule may not be
apparent to the miner, particularly since the volume
and diversity of rules can often be large. However,
given the subjective nature of such sensitivity, rather
than prohibit the production of ethically and privacy
sensitive rules, we present here an alerting process
that detects and highlights the sensitivity of the discovered
rules. The process caters for differing sensitivities
at the attribute value level and allows a variety
of sensitivity combination functions to be employed.
These functions have been tested empirically and the
results of these tests are reported.