  On line analytical processing (OLAP) is an essential element of
decision-support systems. OLAP tools provide insights and understanding needed
for improved decision making. However, the answers to OLAP queries can be
biased and lead to perplexing and incorrect insights. In this paper, we propose
HypDB, a system to detect, explain, and to resolve bias in decision-support
queries. We give a simple definition of a \emph{biased query}, which performs a
set of independence tests on the data to detect bias. We propose a novel
technique that gives explanations for bias, thus assisting an analyst in
understanding what goes on. Additionally, we develop an automated method for
rewriting a biased query into an unbiased query, which shows what the analyst
intended to examine. In a thorough evaluation on several real datasets we show
both the quality and the performance of our techniques, including the
completely automatic discovery of the revolutionary insights from a famous 1973
discrimination case.
