Terminal Prophets

When machines ‘hear’ and ‘understand’ us, it is the result of machine learning programs identifying patterns in voice data, which then inform algorithms to analyse new voice data and make predictions from it. Claire Matthews analysed a database of speech audio files used for teaching machine learning models. The findings point up a number of issues of interpretability, where missing information or ambiguities can result in biased, stereotypical and unreliable results. How this technology is shaping the way knowledge and decisions are produced is explored in a sound essay, alongside which a sound installation plays data extracted by algorithms that mimic those examined.