Algorithms and their performance are a derivative of the efficiency of their design, quality of the data they use as well as the data that they tend to omit – intentionally or due to lack of access to it. Equally important are assumptions made by algorithms to fill the gaps in the data on which they work. As technological and social constructs they are reality-describing models which either remain visible in the process of data analysis or are pushed beyond the limits of our perception. The author discusses ways in which data can be made both visible and transparent, while diagnosing the sources of data omissions and their consequences.
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