The 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 and humanities. 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. As such, the aim of the KPLEX project has been to pinpoint areas where different research communities’ understanding of what the creation of knowledge is and should diverge, and, from this unique perspective, propose where further work can and should be done.
The most compelling outcomes of the project stand at the intersection of the perspectives and themes pursued by these individual work packages and tasks. These points, which cover a wide range of issues around the complexity of the phenomena represented in data and the potential biases inherent in the methods most commonly used to interrogate them, are listed below. The resonances between and across the perspectives mined by the project illuminate those areas where we can evidence fundamental challenges to big data research, or opportunities for innovative future activities. These topics will not be simple to pursue, since some of them are viewed by some key contributors as unnecessary barriers to technical progress. The eleven areas of further research as identified by the project are as follows:
Big data is ill-suited to representing complexity: the urge toward easy integrability can often result in obscurity and user disempowerment.
Learn more: https://kplex-project.eu/
