In a society rapidly turning to artificial intelligence to envision the future, humanity increasingly acknowledges its flaws and limitations: one of the most discussed being algorithmic bias or rather, the systematization of human biases in machine learning. This affects every aspect of society at different extents, yet it is an especially sensitive matter for those who handle images on a regular basis: visual communicators.

This thesis addresses the formation of machine ‘knowledge’ and its manifestation as – time-, place- and culture-bound – visual artifacts, looking at bias not only as harmful but as a structural component of such knowledge, and further investigating how these artifacts are perceived by humans and by machines.

The work aims at displaying some of the hidden corners of such ‘knowledge’ and interrogating their content, investigating how it relates to society and what consequences it could have. Further observing the way in which this technology reflects back onto humanity what humanity has fed it with.