The Annotators at work (Photo: Nicolas Malevé)
Machine-learning algorithms that partially automate data processing still need to be trained for every new form, or every new kind of topic the algorithm might deal with. (…) Such work of alignment is not a bug — it is the condition of possibility for keeping humans and automation working in the same world.1
During Cqrrelations (“poetry to the statistician, science to the dissident and detox to the data-addict”), we developed the pattern.en.paternalism feature.
From the start we were interested in how a Gold Standard is established, a paradoxical situation where human input performs truth, but is simultaneously made invisible. Annotation here means the manual work of ‘scoring’ large amounts of data that can than be used for ‘training’ algorithms. This scored data becomes a reference against which data-mining algorithms are trained and tested.
Read the full report: http://www.cqrrelations.constantvzw.org/1×0/the-annotator/