More Data Can Lead Us Astray: Active Data Acquisition in the Presence of Label Bias

by   Yunyi Li, et al.

An increased awareness concerning risks of algorithmic bias has driven a surge of efforts around bias mitigation strategies. A vast majority of the proposed approaches fall under one of two categories: (1) imposing algorithmic fairness constraints on predictive models, and (2) collecting additional training samples. Most recently and at the intersection of these two categories, methods that propose active learning under fairness constraints have been developed. However, proposed bias mitigation strategies typically overlook the bias presented in the observed labels. In this work, we study fairness considerations of active data collection strategies in the presence of label bias. We first present an overview of different types of label bias in the context of supervised learning systems. We then empirically show that, when overlooking label bias, collecting more data can aggravate bias, and imposing fairness constraints that rely on the observed labels in the data collection process may not address the problem. Our results illustrate the unintended consequences of deploying a model that attempts to mitigate a single type of bias while neglecting others, emphasizing the importance of explicitly differentiating between the types of bias that fairness-aware algorithms aim to address, and highlighting the risks of neglecting label bias during data collection.


Can Active Learning Preemptively Mitigate Fairness Issues?

Dataset bias is one of the prevailing causes of unfairness in machine le...

Mitigating Label Bias via Decoupled Confident Learning

Growing concerns regarding algorithmic fairness have led to a surge in m...

Detection and Mitigation of Bias in Ted Talk Ratings

Unbiased data collection is essential to guaranteeing fairness in artifi...

Why Is My Classifier Discriminatory?

Recent attempts to achieve fairness in predictive models focus on the ba...

Casual Conversations v2: Designing a large consent-driven dataset to measure algorithmic bias and robustness

Developing robust and fair AI systems require datasets with comprehensiv...

Mitigating Bias in Algorithmic Employment Screening: Evaluating Claims and Practices

There has been rapidly growing interest in the use of algorithms for emp...

Detection and Mitigation of Algorithmic Bias via Predictive Rate Parity

Recently, numerous studies have demonstrated the presence of bias in mac...

Please sign up or login with your details

Forgot password? Click here to reset