# Bias Amplification in Artificial Intelligence Systems

arXiv: https://arxiv.org/abs/1809.07842 · HTML: https://mlsanity.com/paper/1809.07842/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2018.

## Built on by (most cited first, 16 of 16)

- [MultiBench: Multiscale Benchmarks for Multimodal Representation Learning](https://mlsanity.com/paper/2107.07502/) (2021)
- [FairTune: Optimizing Parameter Efficient Fine Tuning for Fairness in Medical Image Analysis](https://mlsanity.com/paper/2310.05055/) (2023)
- [ChatGPT as Research Scientist: Probing GPT's Capabilities as a Research Librarian, Research Ethicist, Data Generator and Data Predictor](https://mlsanity.com/paper/2406.14765/) (2024)
- [Leashing the Inner Demons: Self-Detoxification for Language Models](https://mlsanity.com/paper/2203.03072/) (2022)
- [MedEval: A Multi-Level, Multi-Task, and Multi-Domain Medical Benchmark for Language Model Evaluation](https://mlsanity.com/paper/2310.14088/) (2023)
- [Harm Amplification in Text-to-Image Models](https://mlsanity.com/paper/2402.01787/) (2024)
- [IoT-LM: Large Multisensory Language Models for the Internet of Things](https://mlsanity.com/paper/2407.09801/) (2024)
- [Learning Language and Multimodal Privacy-Preserving Markers of Mood from Mobile Data](https://mlsanity.com/paper/2106.13213/) (2021)
- [The (de)biasing effect of GAN-based augmentation methods on skin lesion images](https://mlsanity.com/paper/2206.15182/) (2022)
- [Fairness in AI and Its Long-Term Implications on Society](https://mlsanity.com/paper/2304.09826/) (2023)
- [Fairness and Explainability in Automatic Decision-Making Systems. A challenge for computer science and law](https://mlsanity.com/paper/2206.03226/) (2022)
- [Data augmentation and explainability for bias discovery and mitigation in deep learning](https://mlsanity.com/paper/2308.09464/) (2023)
- [L-PRISMA: An Extension of PRISMA in the Era of Generative Artificial Intelligence (GenAI)](https://mlsanity.com/paper/2603.19236/) (2026)
- [MultiIoT: Benchmarking Machine Learning for the Internet of Things](https://mlsanity.com/paper/2311.06217/) (2023)
- [Bias in the Tails: How Name-conditioned Evaluative Framing in Resume Summaries Destabilizes LLM-based Hiring](https://mlsanity.com/paper/2604.19984/) (2026)
- [Transparent Information Flow Modeling for Role-Aware AI Act Assessment](https://mlsanity.com/paper/2610.16916/) (2026)

## Abstract (arXiv)

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.