The Effects of Data Quality on ML-Model Performance

Modern artificial intelligence (AI) applications require large quantities of training and test data. This need creates critical challenges not only concerning the availability of such data, but also regarding its quality. For example, incomplete, erroneous or inappropriate training data can lead to unreliable models that produce ultimately poor decisions. Trustworthy AI applications require high-quality training and test data along many dimensions, such as accuracy, completeness, consistency, and uniformity. We explore empirically the correlation between six of the traditional data quality dimensions and the performance of fifteen widely used ML algorithms covering the tasks of classification, regression, and clustering, with the goal of explaining ML results in terms of data quality. Our experiments distinguish three scenarios based on the AI pipeline steps that were fed with polluted data: polluted training data, test data, or both. We conclude the paper with an extensive discussion of our observations and recommendations, alongside open questions and future directions to be explored.