Big Data and Complex Knowledge: Observations and Recommendations for Research from the Knowledge Complexity Project
The Knowledge Complexity (or KPLEX) project was created with a two-fold purpose: first, to expose potential areas of bias in big data research, and second, to do so using methods and challenges coming from a research community that has been relatively resistant to big data, namely the arts, the humanities, and the ethnographies. The project’s founding supposition was that there are practical and cultural reasons why humanities research resists datafication, a process generally understood as the substitution of original state research objects and processes for digital, quantified or otherwise more structured streams of information. The project’s further assumption was that these very reasons for resistance could be instructive for the critical observation of big data research and innovation as a whole. To understand clearly the features of humanistic and cultural data, approaches, methodologies, institutions and challenges is to see the fault lines where datafication and algorithmic parsing may fail to deliver on what they promise, or may hide the very insight they propose to expose. As such, the aim of the KPLEX project has been, from the outset, to pinpoint areas where different research communities’ understanding of what the creation of knowledge is and should be diverge, and, from this unique perspective, propose where further work can and should be done.
Vortrag in englischer Sprache.
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29.04.2021 | 14:15
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