How AI Fails: An Interactive Pedagogical Tool for Demonstrating Dialectal Bias in Automated Toxicity Models
arXiv:2511.06676v3 Announce Type: replace Abstract: Now that AI-driven moderation has become pervasive in everyday life, we often hear claims that "the AI is biased". While this is often said jokingly, the light-hearted remark reflects a deeper concern. How can we be certain that an online post flagged as "inappropriate" was not simply the victim of a biased algorithm? This paper investigates this problem using a dual approach. First, I conduct a quantitative benchmark of a widely used toxicity model (unitary/toxic-bert) to measure performance disparity between text in African-American English
The proliferation of AI in moderation systems and public discourse has brought the issue of algorithmic bias to the forefront, necessitating tools for understanding and addressing it.
This paper highlights the critical issue of bias in widely adopted AI models, underscoring the need for robust evaluation and transparency in AI development and deployment for ethical and effective application.
The focus shifts from general claims of AI bias to specific, measurable instances of dialectal bias in toxicity models, providing a concrete example and a pedagogical tool for intervention.
- · AI ethics researchers
- · Diverse linguistic communities
- · Organizations prioritizing fair AI
- · Developers of unexamined 'off-the-shelf' AI models
- · Platforms relying solely on biased automated moderation
- · Monolingual/monocultural AI development paradigms
Increased pressure on AI developers to conduct thorough bias assessments for their models before deployment.
Development of more equitable and inclusive AI models that account for linguistic and cultural variations.
A potential shift in regulatory frameworks demanding explicit bias testing and mitigation strategies for AI systems, particularly in sensitive applications.
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