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Predictive geometric models deliver excellent results for many MachineLearning use cases. Despite their undoubted performance, neural predictivealgorithms can show unexpected degrees of instability and variance,particularly when applied to large datasets. We present an approach to measurechanges in geometric models with respect to both output consistency andtopological stability. Considering the example of a recommender system usingword2vec, we analyze the influence of single data points, approximation methodsand parameter settings. Our findings can help to stabilize models where neededand to detect differences in informational value of data points on a largescale.

Analyzing Hypersensitive AI: Instability in Corporate-Scale Machine Learning