Computational Philosophy

Abstract

The critical value of interdisciplinarity is increasingly accepted not only for promoting the responsible use of machine learning models, but also for increasing their performance. Social scientists can then help engineers better understand the population they are gathering data from. To be successful, however, interdisciplinary collaborations require more than just gathering researchers from various fields around a table. They call for addressing challenges such as developing a common language, understanding different ways of reasoning, and addressing epistemic controversies to agree on shared criteria upon which can be assessed the validity of the co-produced knowledge. Controversies bring about relevant epistemic questions to promote an ongoing reflection on scientific methods. In contrast, when a discipline leverages its own methods to approach a topic traditionally associated with another discipline, the unsolicited interference is often followed by a backlash which undermines the possibility of collaboration (e.g., [1], [2], [3]).

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