The Impact of Features Used by Algorithms on Perceptions of Fairness

We investigate perceptions of fairness in the choice of features that algorithms use about individuals in a simulated gigwork employment experiment. First, a collection of experimental participants (the selectors) were asked to recommend an algorithm for making employment decisions. Second, a different collection of participants (the workers) were told about the setup, and a subset were ostensibly selected by the algorithm to perform an image labeling task. For both selector and worker participants, algorithmic choices differed principally in the inclusion of features that were non-volitional, and either directly relevant to the task, or for which relevance is not evident except for these features resulting in higher accuracy. We find that the selectors had a clear predilection for the more accurate algorithms, which they also judged as more fair. Worker sentiments were considerably more nuanced. Workers who were hired were largely indifferent among the algorithms. In contrast, workers who were not hired exhibited considerably more positive sentiments for algorithms that included non-volitional but relevant features. However, workers with disadvantaged values of non-volitional features exhibited more negative sentiment towards their use than the average, although the extent of this appears to depend considerably on the nature of such features.