Bias Amplification in Artificial Intelligence Systems
This highlights a critical societal issue for marginalized groups and policymakers, but it is incremental as it builds on existing concerns about bias in AI without introducing new technical solutions.
The paper addresses the problem of AI systems amplifying bias from training datasets, which disproportionately impacts marginalized populations at scale, and argues for government and public sector action to establish data standards and policies to ensure diverse representation and inclusion.
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.