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The global infrastructure of the Web, designed as an open and transparentsystem, has a significant impact on our society. However, algorithmic systemsof corporate entities that neglect those principles increasingly populated theWeb. Typical representatives of these algorithmic systems are recommendersystems that influence our society both on a scale of global politics andduring mundane shopping decisions. Recently, such recommender systems have comeunder critique for how they may strengthen existing or even generate new kindsof biases. To this end, designers and engineers are increasingly urged to makethe functioning and purpose of recommender systems more transparent. Ourresearch relates to the discourse of algorithm awareness, that reconsiders therole of algorithm visibility in interface design. We conducted onlineexperiments with 105 participants using MTurk for the recommender systemRecoin, a gadget for Wikidata. In these experiments, we presented users withone of a set of three different designs of Recoin's user interface, each ofthem exhibiting a varying degree of explainability and interactivity. Ourfindings include a positive correlation between comprehension of and trust inan algorithmic system in our interactive redesign. However, our results are notconclusive yet, and suggest that the measures of comprehension, fairness,accuracy and trust are not yet exhaustive for the empirical study of algorithmawareness. Our qualitative insights provide a first indication for furthermeasures. Our study participants, for example, were less concerned with thedetails of understanding an algorithmic calculation than with who or what isjudging the result of the algorithm.

Examining the Impact of Algorithm Awareness on Wikidata's Recommender System Recoin