(De)Noise: Moderating the Inconsistency Between Human Decision-Makers

TL;DR

This study investigates how algorithmic decision aids can reduce inconsistency in human judgments, particularly in real estate appraisal, by testing different review strategies that improve accuracy and consistency.

Contribution

It demonstrates that reviewing estimates through algorithmically selected comparisons or traditional machine advice enhances decision consistency and accuracy in human real estate appraisals.

Findings

Algorithmic assistance increases estimate updates.

Reviewing with pairwise comparisons improves consistency.

Traditional machine advice yields higher accuracy.

Abstract

Prior research in psychology has found that people's decisions are often inconsistent. An individual's decisions vary across time, and decisions vary even more across people. Inconsistencies have been identified not only in subjective matters, like matters of taste, but also in settings one might expect to be more objective, such as sentencing, job performance evaluations, or real estate appraisals. In our study, we explore whether algorithmic decision aids can be used to moderate the degree of inconsistency in human decision-making in the context of real estate appraisal. In a large-scale human-subject experiment, we study how different forms of algorithmic assistance influence the way that people review and update their estimates of real estate prices. We find that both (i) asking respondents to review their estimates in a series of algorithmically chosen pairwise comparisons and (ii) providing respondents with traditional machine advice are effective strategies for influencing human responses. Compared to simply reviewing initial estimates one by one, the aforementioned strategies lead to (i) a higher propensity to update initial estimates, (ii) a higher accuracy of post-review estimates, and (iii) a higher degree of consistency between the post-review estimates of different respondents. While these effects are more pronounced with traditional machine advice, the approach of reviewing algorithmically chosen pairs can be implemented in a wider range of settings, since it does not require access to ground truth data.