Demographic Biases of Crowd Workers in Key Opinion Leaders Finding

TL;DR

This paper addresses demographic biases in crowdsourcing for identifying Key Opinion Leaders by proposing a method to measure and mitigate worker biases using demographic data and counterfactual analysis.

Contribution

It introduces a novel approach that leverages demographic information and counterfactual values to assess and reduce biases in crowd-sourced KOL finding tasks.

Findings

The proposed method effectively measures individual worker biases.

Using demographic data improves the quality of KOL identification.

The approach helps curate less biased datasets for better decision-making.

Abstract

Key Opinion Leaders (KOLs) are people that have a strong influence and their opinions are listened to by people when making important decisions. Crowdsourcing provides an efficient and cost-effective means to gather data for the KOL finding task. However, data collected through crowdsourcing is affected by the inherent demographic biases of crowd workers. To avoid such demographic biases, we need to measure how biased each crowd worker is. In this paper, we propose a simple yet effective approach based on demographic information of candidate KOLs and their counterfactual value. We argue that it is effectiveness because of the extra information that we can consider together with labeled data to curate a less biased dataset.