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Bias Amplification in Artificial Intelligence Systems

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arxiv 1809.07842 v1 pith:2GN6RHVG submitted 2018-09-20 cs.AI

classification cs.AI
keywords biasamplificationaroundartificialconcerndatasetsensureintelligence
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 43 citations worldwide. Full citation record

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    cs.CY 2025-01 conditional novelty 6.0 of 10

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