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Abstract

The global infrastructure of the Web, designed as an open and transparent
system, has a significant impact on our society. However, algorithmic systems
of corporate entities that neglect those principles increasingly populated the
Web. Typical representatives of these algorithmic systems are recommender
systems that influence our society both on a scale of global politics and
during mundane shopping decisions. Recently, such recommender systems have come
under critique for how they may strengthen existing or even generate new kinds
of biases. To this end, designers and engineers are increasingly urged to make
the functioning and purpose of recommender systems more transparent. Our
research relates to the discourse of algorithm awareness, that reconsiders the
role of algorithm visibility in interface design. We conducted online
experiments with 105 participants using MTurk for the recommender system
Recoin, a gadget for Wikidata. In these experiments, we presented users with
one of a set of three different designs of Recoin's user interface, each of
them exhibiting a varying degree of explainability and interactivity. Our
findings include a positive correlation between comprehension of and trust in
an algorithmic system in our interactive redesign. However, our results are not
conclusive yet, and suggest that the measures of comprehension, fairness,
accuracy and trust are not yet exhaustive for the empirical study of algorithm
awareness. Our qualitative insights provide a first indication for further
measures. Our study participants, for example, were less concerned with the
details of understanding an algorithmic calculation than with who or what is
judging the result of the algorithm.