The decision-making process of many state-of-the-art machine learning models is inherently inscrutable to the extent that it is impossible for a human to interpret the model directly: they are black box models. This has led to a call for research on explaining black box models, for which there are two main approaches. Global explanations that aim to explain a model's decision making process in general, and local explanations that aim to explain a single prediction. Since it remains challenging to establish fidelity to black box models in globally interpretable approximations, much attention is put on local explanations. However, whether local explanations are able to reliably represent the black box model and provide useful insights remains an open question. We present Global Aggregations of Local Explanations (GALE) with the objective to provide insights in a model's global decision making process. Overall, our results reveal that the choice of aggregation matters. We find that the global importance introduced by Local Interpretable Model-agnostic Explanations (LIME) does not reliably represent the model's global behavior. Our proposed aggregations are better able to represent how features affect the model's predictions, and to provide global insights by identifying distinguishing features.

Greening up the chemical industry by using waste biomass streams as feed is a topic of high relevance. Residual lignins from for example the pulp and paper industry and second-generation...

The purpose of the SIGIR 2019 workshop on Fairness, Accountability, Confidentiality, Transparency, and Safety (FACTS-IR) was to explore challenges in responsible information retrieval system development and deployment. To this end, the workshop aimed to crowd-source from the larger SIGIR community and draft an actionable research agenda on five key dimensions of responsible information retrieval: fairness, accountability, confidentiality, transparency, and safety. Such an agenda can guide others in the community that are interested in pursuing FACTS-IR research, as well as inform potential funders about relevant research avenues. The workshop brought together a diverse set of researchers and practitioners interested in contributing to the development of a technical research agenda for responsible information retrieval.