SOCIOLINGUISTICS, ARTIFICIAL INTELLIGENCE, CULTURAL COMPUTING
From Culture to Code: An Intersectional Analysis of AAVE Slang in Large Language Models
This study rigorously evaluates how Large Language Models (LLMs) interpret African American Vernacular English (AAVE) slang, revealing significant inconsistencies in contextual understanding, particularly for culturally rich subcategories. It highlights the critical need for inclusive AI development, diversified datasets, and community-centered approaches to ensure equitable representation of Black American linguistic traditions.
Executive Impact & Core Insights
Our analysis of LLM performance on AAVE slang reveals crucial metrics for enterprises aiming for linguistically equitable AI. Understanding these insights is key to developing responsible and culturally aware AI systems.
75%
LLM MISINTERPRETATION RATE FOR CONTEXTUALLY RICH SLANG
0.5
VARIABILITY IN APPROPRIATENESS SCORES FOR QUEER AAVE
Avg 2.58
LOWEST ACCURACY SCORE FOR COLLEGIATE SLANG
Top 20
PERCENTAGE OF SLANG TERMS WITH LOWEST SCORES
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Sociolinguistics
Understanding AAVE as a legitimate, rule-governed linguistic system is crucial for accurate AI processing. This category explores the structural and cultural significance of AAVE, emphasizing its dynamic evolution and diverse subcultures.
AI Bias
AI systems, particularly LLMs, often exhibit linguistic biases, misinterpreting or marginalizing non-dominant language varieties like AAVE. This section details how these biases manifest in content moderation, sentiment analysis, and overall representation, leading to inequitable outcomes for Black users.
Cultural Representation
AAVE slang is a key source of linguistic innovation, yet AI systems often appropriate or misrepresent it, stripping terms of their cultural context. This category examines how AI contributes to the erasure and commodification of Black linguistic creativity, reinforcing stereotypes rather than respecting nuanced expression.
Intersectional Approach
AAVE is not monolithic; it varies by region, gender, age, sexuality, and class. An intersectional lens is vital for understanding these variations, especially within Black LGBTQ+ and collegiate communities. AI models often fail to capture this diversity, homogenizing the dialect and reinforcing biases.
Data Ethics
The ethical development of AI requires culturally aware practices, including diverse training datasets, community-centered annotation, and bias mitigation strategies. This category highlights the need for AI systems to respect and equitably represent all forms of linguistic expression, moving beyond mere bias identification to linguistic justice.
Literality
LLMs struggle with figurative language, often providing literal interpretations of AAVE slang, stripping terms of cultural context.
Enterprise Process Flow
Identify diverse AAVE subcultures
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Collect culturally significant slang terms
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Label terms with meaning & usage context
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Query LLMs with standardized prompts
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Evaluate LLM outputs for Accuracy, Context, Appropriateness
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Identify patterns of misinterpretation & bias
| LLM Type | Strengths in AAVE Processing | Weaknesses in AAVE Processing |
|---|---|---|
| GPT-40 (OpenAI) |
|
|
| Claude 3.5 (Anthropic) |
|
|
| Llama 2 (Meta) |
|
|
Case Study: Misattribution of Black Greek Life Terminology
LLMs consistently failed to correctly associate Black Greek Letter Organization (BGLO) slang with its proper fraternity or sorority. For instance, "pretty girl" was often returned as a generic compliment instead of its specific meaning as a nickname for Alpha Kappa Alpha Sorority, Inc. members. Similarly, "the dawgs" was misattributed to Alpha Phi Alpha instead of Omega Psi Phi (Q-Dawgs). This pervasive error indicates a profound lack of BGLO-specific training data or an inability to differentiate between distinct Black collegiate organizations, leading to misleading and culturally inaccurate outputs.
Key Takeaway: AI models must be trained with specialized, culturally specific datasets and alignment strategies to avoid misattributions and reinforce accurate cultural representation, particularly in highly contextualized communities like BGLOs.
Emoji-based
LLMs often misinterpret culturally specific emoji usage, relying on default Unicode meanings instead of digital Black discourse.
Case Study: Overgeneralization and Outdated Contexts
LLMs frequently recognized AAVE terms at a superficial level but failed to provide correct cultural grounding, resulting in broad, outdated, or contextually removed definitions. For example, "big back" was misinterpreted as a financial status marker, ignoring its common playful reference to body size. "Little" was defined generically, missing its specific meaning in fraternity/sorority culture as a mentee. These errors highlight LLMs' over-reliance on standardized English definitions and their failure to track the dynamic evolution of AAVE terms across diverse social and cultural contexts.
Key Takeaway: AI systems need continuous, culturally informed updates and fine-tuning to reflect contemporary usage and avoid outdated or generic interpretations that strip AAVE terms of their nuanced, community-specific meanings.
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Our AI Implementation Roadmap for Linguistic Equity
We partner with you to integrate culturally aware AI, ensuring your systems are accurate, respectful, and inclusive. Our phased approach guarantees a seamless transition and measurable impact.
Discovery & Linguistic Audit
Comprehensive assessment of your current AI systems and target user base to identify linguistic diversity gaps. This includes an AAVE-specific audit and stakeholder interviews.
Custom Dataset Curation & Annotation
Development of specialized training datasets with diverse AAVE examples, including slang, multimodal communication, and context, leveraging community-centered annotation.
Model Fine-Tuning & Bias Mitigation
Fine-tuning LLMs with culturally informed data and applying bias mitigation techniques to improve accuracy and appropriateness for AAVE, ensuring nuanced understanding.
Integration & Performance Monitoring
Seamless integration of enhanced AI models into your existing platforms. Ongoing monitoring and feedback loops with AAVE speakers to ensure sustained linguistic equity and performance.
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