# Evaluating Artificial Systems for Pairwise Ranking Tasks Sensitive to Individual Differences

## TL;DR

This paper introduces a new evaluation method for artificial systems performing pairwise ranking tasks that are sensitive to individual differences, using a probabilistic model to compare system outputs with human judgments.

## Contribution

The paper proposes a probabilistic evaluation framework for ranking tasks with individual differences, incorporating confidence scores to improve parameter estimation from small samples.

## Key findings

- The method effectively quantifies the similarity between artificial and human rankings.
- It can distinguish human-like rankings from non-human-like results.
- Application to material attribute ranking demonstrates practical utility.

## Abstract

Owing to the advancement of deep learning, artificial systems are now rival to humans in several pattern recognition tasks, such as visual recognition of object categories. However, this is only the case with the tasks for which correct answers exist independent of human perception. There is another type of tasks for which what to predict is human perception itself, in which there are often individual differences. Then, there are no longer single "correct" answers to predict, which makes evaluation of artificial systems difficult. In this paper, focusing on pairwise ranking tasks sensitive to individual differences, we propose an evaluation method. Given a ranking result for multiple item pairs that is generated by an artificial system, our method quantifies the probability that the same ranking result will be generated by humans, and judges if it is distinguishable from human-generated results. We introduce a probabilistic model of human ranking behavior, and present an efficient computation method for the judgment. To estimate model parameters accurately from small-size samples, we present a method that uses confidence scores given by annotators for ranking each item pair. Taking as an example a task of ranking image pairs according to material attributes of objects, we demonstrate how the proposed method works.