A Hundred Thousand Lousy Cats (exploring drawing, AI and creativity)

This paper introduces a practice-led project that uses the Google Quick, Draw! project and dataset to explore the potential differences of algorithmic machine or digitally constructed drawings, and fictional associative hand-drawings. The authors use both digital 20-second sketching (the rule set for the Quick, Draw! Project) and more elaborate drawings and collages to then analyse and speculate about the results of these types of visualisations. At this phase of research it seems obvious to label and move the machine drawing to the reductive, the handdrawn to the more complex and associative realm but we seek to unpack this binary. Artificial intelligence and machine-learning are producing a wealth of creative projects, we select a couple of case studies to speak to particular visual artefacts that derive from algorithmic processing. For instance, the (IBM AI) Watson-composed film trailer for Morgan is considered as a creative artefact and looked at for its apparent allure and effect on a creative process. Through this inquiry we contemplate surprises and mistakes that come naturally when producing hand-made works, exploring then, what it means to draw and to work within classification systems in an algorithm-leaning world

Visualizing Junk: Big Data Visualizations and the need for Feminist Data Studies

The datafication of culture has led to an increase in the circulation of data visualizations. In their production, visualizers draw on historical antecedents which define what constitutes a good visualization. In their reception, audiences similarly draw on experiences with visualizations and other visual forms to categorize them as good or bad. Whilst there are often sound reasons for such assessments, the gendered dimensions of judgements of cultural artefacts like data visualizations cannot be ignored. In this paper, we highlight how definitions of visualizations as bad are sometimes gendered. In turn, this gendered derision is often entangled with legitimate criticisms of poor visualization execution, making it hard to see and so normalised. This, we argue, is a form of what Gill (2011) calls flexible sexism, and it is why there is a need not just for feminist critiques of big data, but for feminist data studies – that is, feminists doing big data and data visualization