Bias Amplification in Artificial Intelligence Systems

TL;DR

This paper highlights how AI systems can amplify existing biases in training data, posing immediate risks to marginalized groups, and emphasizes the need for policy and data standards to promote fairness and inclusion.

Contribution

It draws attention to bias amplification in AI and advocates for policy measures and diverse data standards to mitigate societal harm.

Findings

AI can significantly amplify biases present in training data

Bias amplification disproportionately affects marginalized populations

Policy and data standards are essential for fair AI development

Abstract

As Artificial Intelligence (AI) technologies proliferate, concern has centered around the long-term dangers of job loss or threats of machines causing harm to humans. All of this concern, however, detracts from the more pertinent and already existing threats posed by AI today: its ability to amplify bias found in training datasets, and swiftly impact marginalized populations at scale. Government and public sector institutions have a responsibility to citizens to establish a dialogue with technology developers and release thoughtful policy around data standards to ensure diverse representation in datasets to prevent bias amplification and ensure that AI systems are built with inclusion in mind.