  Research on bias in machine learning algorithms has generally been concerned
with the impact of bias on predictive accuracy. We believe that there are other
factors that should also play a role in the evaluation of bias. One such factor
is the stability of the algorithm; in other words, the repeatability of the
results. If we obtain two sets of data from the same phenomenon, with the same
underlying probability distribution, then we would like our learning algorithm
to induce approximately the same concepts from both sets of data. This paper
introduces a method for quantifying stability, based on a measure of the
agreement between concepts. We also discuss the relationships among stability,
predictive accuracy, and bias.
