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Owing to the advancement of deep learning, artificial systems are now rivalto humans in several pattern recognition tasks, such as visual recognition ofobject categories. However, this is only the case with the tasks for whichcorrect answers exist independent of human perception. There is another type oftasks for which what to predict is human perception itself, in which there areoften individual differences. Then, there are no longer single "correct"answers to predict, which makes evaluation of artificial systems difficult. Inthis paper, focusing on pairwise ranking tasks sensitive to individualdifferences, we propose an evaluation method. Given a ranking result formultiple item pairs that is generated by an artificial system, our methodquantifies the probability that the same ranking result will be generated byhumans, and judges if it is distinguishable from human-generated results. Weintroduce a probabilistic model of human ranking behavior, and present anefficient computation method for the judgment. To estimate model parametersaccurately from small-size samples, we present a method that uses confidencescores given by annotators for ranking each item pair. Taking as an example atask of ranking image pairs according to material attributes of objects, wedemonstrate how the proposed method works.

Evaluating Artificial Systems for Pairwise Ranking Tasks Sensitive to Individual Differences