interpretable-ml.org
Christopher Mutschler
This webpage aims to regroup publications and software produced as part of a joint project at Fraunhofer HHI and TU Berlin on developing new methods to understand nonlinear predictions of state-of-the-art machine learning models.
Machine learning models, in particular deep neural networks (DNNs), are characterized by very high predictive power, but in many case, are not easily interpretable by a human. Interpreting a nonlinear classifier is important to gain trust into the prediction, and to identify potential data selection biases or artefacts.
The project studies in particular techniques to decompose the prediction in terms of contributions of individual input variables such that the produced decomposition (i.e. explanation) can be visualized in the same way as the input data.
interpretable-ml.org
Christopher Mutschler