Workshop paper
Bhavya Ghai, Q. Vera Liao, Yunfeng Zhang, Klaus Mueller
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APA
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Ghai, B., Liao, Q. V., Zhang, Y., & Mueller, K. Measuring Social Biases of Crowd Workers using Counterfactual Queries.
Chicago/Turabian
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Ghai, Bhavya, Q. Vera Liao, Yunfeng Zhang, and Klaus Mueller. Measuring Social Biases of Crowd Workers Using Counterfactual Queries, n.d.
MLA
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Ghai, Bhavya, et al. Measuring Social Biases of Crowd Workers Using Counterfactual Queries.
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@techreport{bhavya-a,
title = {Measuring Social Biases of Crowd Workers using Counterfactual Queries},
author = {Ghai, Bhavya and Liao, Q. Vera and Zhang, Yunfeng and Mueller, Klaus}
}
Abstract
Social biases based on gender, race, etc. have been shown to pollute machine learning (ML) pipeline predominantly via biased training datasets. Crowdsourcing, a popular cost-effective measure to gather labeled training datasets, is not immune to the inherent social biases of crowd workers. To ensure such social biases aren't passed onto the curated datasets, it's important to know how biased each crowd worker is. In this work, we propose a new method based on counterfactual fairness to quantify the degree of inherent social bias in each crowd worker. This extra information can be leveraged together with individual worker responses to curate a less biased dataset.
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Social biases based on gender, race, etc. have been shown to pollute machine learning (ML) pipeline predominantly via biased training datasets. Crowdsourcing, a popular cost-effective measure to gather labeled training datasets, is not immune to the inherent social biases of crowd workers. To ensure such social biases aren't passed onto the curated datasets, it's important to know how biased each crowd worker is. In this work, we propose a new method based on counterfactual fairness to quantify the degree of inherent social bias in each crowd worker. This extra information can be leveraged together with individual worker responses to curate a less biased dataset.
