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Are AI systems biased against the poor? A machine learning analysis using Word2Vec and GloVe embeddings
Georgina Curto1,2, Mario Fernando Jojoa Acosta3, Flavio Comim1
1Universitat Ramon Llull, IQS School of Management, Barcelona, Spain.
AI & Society
|July 5, 2022
Summary
This study reveals AI bias against the poor using Natural Language Processing (NLP) word embeddings. Google Word2Vec and Twitter GloVe data show bias related to beliefs and behaviors, impacting poverty reduction efforts.
Area of Science:
- Interdisciplinary research combining Artificial Intelligence (AI) and social sciences.
- Focus on societal bias, specifically against impoverished populations.
Background:
- Growing concern over AI bias necessitates translating fairness principles into practical AI applications.
- Lack of generalizable solutions for AI bias requires context-specific analysis involving social scientists.
Purpose of the Study:
- To offer an interdisciplinary framework and model for analyzing AI bias against the poor.
- To provide empirical evidence of AI bias against impoverished groups using NLP.
Main Methods:
- Utilized Natural Language Processing (NLP) word vectors from pretrained Google Word2Vec and Twitter/Wikipedia GloVe embeddings.
- Developed a tailor-made model to extract meaningful data on AI bias.
Main Results:
- Presented the first dataset evidencing AI bias against the poor.
- Google Word2Vec exhibited higher bias for belief-related terms; Twitter GloVe showed higher bias for behavior-related terms.
Conclusions:
- AI bias against the poor acts as a transversal aggravating factor for historical discrimination.
- This bias has significant implications for human development and the efficacy of poverty reduction policies.
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