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.

中文翻译：

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检查算法意识对维基数据推荐系统的影响 Recoin

网络的全球基础设施设计为一个开放和透明的系统，对我们的社会产生了重大影响。然而，忽略这些原则的公司实体的算法系统越来越多地出现在 Web 上。这些算法系统的典型代表是推荐系统，它们在全球政治规模和平凡的购物决策中影响我们的社会。最近，此类推荐系统因如何加强现有甚至产生新的偏见而受到批评。为此，越来越多的设计师和工程师被敦促使推荐系统的功能和目的更加透明。我们的研究涉及算法意识的话语，重新考虑算法可见性在界面设计中的作用。我们对 105 名参与者使用 MTurk 作为推荐系统 Recoin（维基数据的小工具）进行了在线实验。在这些实验中，我们向用户展示了 Recoin 用户界面的三种不同设计中的一种，每种设计都表现出不同程度的可解释性和交互性。我们的发现包括在我们的交互式重新设计中对算法系统的理解和信任之间存在正相关。然而，我们的结果还没有定论，并表明理解性、公平性、准确性和信任度的衡量标准对于算法意识的实证研究还不够详尽。我们的定性见解为进一步措施提供了第一个迹象。例如，我们的研究参与者，