Technical Note: Bias and the Quantification of Stability

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. Key Words: stability, bias, accuracy, repeatability, agreement, similarity. Running Head: Bias and the Quantification of Stability Submitted to: Machine Learning, Special Issue on Bias Evaluation and Selection 

• Type: Journal
• Year: 2002
• Where: CORR
• Authors: Peter D. Turney
• Keywords: Algorithm, Bias, CORR 2002, Education, Predictive Accuracy