Data, Security, Values: Vocations and Visions of Data Analysis. Peace Research Institute Oslo (PRIO)., 2018
With the development of a critical research agenda on contemporary data practices we gradually bu... more With the development of a critical research agenda on contemporary data practices we gradually build the tools that are needed to overcome the uncertainty, lack of clarity, and impact of misleading narratives concerning the epistemology of data science. Without such a reflection, we cannot understand the kind of knowledge data analysis produces. More importantly, we then also lack the ability to evaluate specific knowledge-claims as well as more general affirmations of the epistemic superiority (smarter, more objective, ...) of the knowledge, decisions, or insights that data analysis produces. This is why it is important to recognise that data is never just data (e.g. Gitelman 2013, Kitchin 2014), or that the development of algorithms (as any advanced scientific or engineering practice) cannot fully be understood in terms of a well-defined internal logic.
The starting point of this contribution is that we should start asking similar questions about mathematics: We need to understand how mathematics contributes to scientific respectability and authority of data science. To do so, we cannot limit our attention to mathematics as a body of mathematical truths or mathematical techniques. Instead, we should focus on mathematical thought and beliefs about the nature of mathematical thought. I propose to develop this critical inquiry through a dedicated consideration of how mathematical values shape data science.
Uploads
Papers by Patrick Allo
Using these disjunctive messages as an example, we answer three traditional objections to substructural logic and logical pluralism, and eventually show that the linear or relevant logician’s road to unambiguous connectives is consistent with informational pluralism.
Using these disjunctive messages as an example, we answer three traditional objections to substructural logic and logical pluralism, and eventually show that the linear or relevant logician’s road to unambiguous connectives is consistent with informational pluralism.
The book covers all the main topics of the Philosophy of Information and it should be considered an overview and not a comprehensive, in-depth analysis of a philosophical area. As a consequence, 'The Philosophy of Information: a Simple Introduction' does not contain research material as it is not aimed at graduate students or researchers.
The book is available for free in multiple formats and it is updated every twelve months by the team of the π Research Network: Patrick Allo, Bert Baumgaertner, Simon D'Alfonso, Penny Driscoll, Luciano Floridi, Nir Fresco, Carson Grubaugh, Phyllis Illari, Eric Kerr, Giuseppe Primiero, Federica Russo, Christoph Schulz, Mariarosaria Taddeo, Matteo Turilli, Orlin Vakarelov.
Beta version published 2012, first version published 2013.
The starting point of this contribution is that we should start asking similar questions about mathematics: We need to understand how mathematics contributes to scientific respectability and authority of data science. To do so, we cannot limit our attention to mathematics as a body of mathematical truths or mathematical techniques. Instead, we should focus on mathematical thought and beliefs about the nature of mathematical thought. I propose to develop this critical inquiry through a dedicated consideration of how mathematical values shape data science.