Simple idealized models seem to provide more understanding than opaque, complex, and hyperrealistic models. However, an increasing number of scientists are going in the opposite direction by utilizing opaque machine learning models to make predictions and draw inferences, suggesting that scientists are opting for models that have less potential for understanding. Are scientists trading understanding for some other epistemic or pragmatic good when they choose a machine learning model? Or are the assumptions behind why minimal models provide understanding misguided? In this article, using the case of deep neural networks, I argue that it is not the complexity or black box nature of a model that limits how much understanding the model provides. Instead, it is a lack of scientific and empirical evidence supporting the link that connects a model to the target phenomenon that primarily prohibits understanding.

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Because idealizations frequently advance scientific understanding, many claim that falsehoods play an epistemic role. In this paper, we argue that these positions greatly overstate idealizations’ import for understanding. We introduce work on epistemic value to the debate surrounding idealizations and understanding, arguing that idealizations qua falsehoods confer only non-epistemic value to understanding. We argue for this claim by criticizing the leading accounts of how idealizations provide understanding. For each of these approaches, we show that: (a) idealizations’ false components promote only convenience instead of understanding and (b) only the true components of idealizations have epistemic value.

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Personalized content provided by recommender systems is an integral part of the current online news reading experience. However, news recommender systems are criticized for their'black-box' approach to data collection and processing, and for their lack of explainability and transparency. This paper focuses on explaining user profiles constructed from aggregated reading behavior data, used to provide content-based recommendations. The paper makes a first step toward consolidating epistemic values of news providers and news readers. We present an evaluation of an explanation interface reflecting these values, and find that providing users with different goals for self-actualization (i.e., Broaden Horizons vs. Discover the Unexplored) influences their reading intentions for news recommendations.

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Recently, many have argued that there are certain kinds of abstract mathematical explanations that are noncausal. In particular, the irrelevancy approach suggests that abstracting away irrelevant causal details can leave us with a noncausal explanation. In this paper, I argue that the common example of Renormalization Group (RG) explanations of universality used to motivate the irrelevancy approach deserves more critical attention. I argue that the reasons given by those who hold up RG as noncausal do not stand up to critical scrutiny. As a result, the irrelevancy approach and the line between casual and noncausal explanation deserves more scrutiny.@en

Recommender systems for news articles on social media select and filter content through automatic personalization. As a result, users are often unaware of opposing points of view, leading to informational blindspots and potentially polarized opinions. They may be aware of a topic, but only be exposed to one viewpoint on this topic. However, recommender systems have just as much potential to help users find a plurality of viewpoints. In this spirit, this paper introduces an approach to automatically identifying content that represents a wider range of opinions on a given topic. Our offline results show positive results for our distance measure with regard to diversification on topic and channel. However, our user study results confirm that user acceptance of this diversification also needs to be addressed in tandem to enable a complete solution.@en

For the period surrounding the 2018 Dutch municipal elections, a team of researchers from the Delft University of Technology investigated the effect of the digital environment on parliamentary democracy. An interdisciplinary group of researchers combined expertise on digital ethics, political theory, big data analytics, the economics of privacy and security, epistemology, media studies and computer science. This report presents the main findings, which are grouped around two main themes: political micro-targeting and ICT media. Societal themes that came to prominence over the research period, such as the debate over ‘fake news’ and the leaks of personal information that were used for political purposes by Facebook, as well as the implementation of new EU privacy regulation helped to put the research in a larger political context. The main findings provide a qualified picture. The influence of the digital revolution on democratic politics is already revolutionary, and the weaknesses of online platforms provide ample opportunities for derailing liberal democracy. Digital platforms are too closed-off, not mindful enough of individual digital rights, and biased in their (re)presentation of political pluralism. But the Netherlands has proven to be one of the few democracies that is relatively resilient, with an open multi-party system receptive to the political fragmentation that ICT developments encourage, and relatively high trust between citizens, in shared media organizations, and between political parties. In order not to be complacent in the face of fundamental challenges, the report provides several urgent recommendations. Next to several ‘reactive’ recommendations, which seek to remedy the weaknesses and dangers the digital environment poses to democracy, it also outlines an example of how the digital environment might be proactively redesigned in order to positively enhance the quality of the Dutch parliamentary system. @en