Representation Harms › Erasure

Erasure is the absence or systematic underrepresentation of a group in generated text. Where stereotyping misrepresents groups, erasure makes them invisible. The harm is different: instead of false or degrading associations, erasure conveys that certain people, cultures, or experiences don't exist, aren't relevant, or aren't worth acknowledging.

The most direct form of erasure is simply who appears in generated text. When asked to generate stories about "a doctor and a patient," "a couple enjoying their vacation," or "a group of friends," models produce content centered on particular demographics far more often than others. White, male, and Western characters dominate across many generation scenarios even when the prompt contains no demographic specification.

This matters because these defaults shape what users see as normal, expected, or central. A student using an AI writing assistant who repeatedly gets content centered on one demographic group is receiving implicit signals about whose stories are worth telling. The signal isn't delivered through any single output. It accumulates over many interactions, each one subtly calibrating the user's sense of which voices are default and which require explicit specification to appear.

At scale, the effect compounds further: millions of users receiving consistently biased defaults over millions of interactions creates a feedback loop where AI-generated text becomes part of the information environment that future models are trained on. Erasure that enters training data propagates forward into future models unless actively measured and corrected. This compounding affects model training cycles and the broader cultural environment in which people form beliefs about who belongs in which roles.

Language models trained primarily on English internet data are dramatically more capable in English than in other languages, and within English, more capable with text from dominant cultural contexts. But erasure happens at a finer grain as well. Dialects, registers, and cultural concepts from non-dominant groups receive systematically worse treatment:

• African American Vernacular English (AAVE) is more likely to be flagged as lower quality or "incorrect" by language model-based quality tools
• Models perform worse at sentiment analysis and named entity recognition on text from non-Western cultural contexts
• Languages with fewer speakers, especially indigenous languages, receive almost no coverage
• Code-switching and multilingual communication patterns from bilingual communities are handled poorly

The linguistic erasure of AAVE is particularly consequential because AAVE is not a degraded form of Standard American English. It is a complete, rule-governed dialect with its own consistent grammar, phonology, and pragmatic conventions. Models that treat AAVE as inferior or incorrect aren't making a linguistic judgment. They're reproducing a social hierarchy that has historically been used to invalidate Black American speech, culture, and authority. When a language model's writing quality scorer gives lower scores to AAVE text, or when a grammar checker flags AAVE constructions as errors, the tool is acting as an instrument of linguistic oppression regardless of the intent of its designers.

The solution is not to make models treat AAVE exactly like Standard American English in all contexts, since the two dialects differ and sometimes the distinction matters. The solution is to ensure that models treat AAVE as a legitimate, rule-governed dialect whose conventions are respected rather than pathologized, and that quality scores don't systematically discount text written in non-dominant varieties.

When models generate historical narratives, scientific histories, or biographical content, they systematically underrepresent contributions from women, people of color, and non-Western civilizations. Probes asking models to "list important mathematicians" or "describe the history of this scientific field" produce outputs that reflect the biases of English-language textbooks and Wikipedia more than actual historical contributions.

This form of erasure has a compounding mechanism of its own. Historical narratives shape how people understand what is possible. If a model consistently fails to mention that al-Khwarizmi founded algebra, that Katherine Johnson calculated orbital mechanics by hand for early NASA missions, or that Islamic scholars preserved and advanced Greek mathematics during the European Dark Ages, it is shaping users' understanding of who has contributed to human knowledge. Over millions of interactions with millions of users, that shaping effect is real.

The underlying cause is the training data distribution: English-language textbooks and Wikipedia, which reflect decades of scholarship selection, themselves underrepresent non-Western and non-male contributions. Fixing this at the model level requires either curating training data to include more representative historical sources, or applying targeted finetuning on corrected historical information, or both.

Machine translation systems present a particularly sharp form of erasure. When translating from languages with grammatical gender into English, or from English into languages with grammatical gender, models must make gender attribution decisions. These decisions systematically default to masculine for occupational roles and certain contexts.

The most documented case involves translating from gender-neutral languages like Finnish, Hungarian, or Turkish into languages with grammatical gender like Spanish or French. A sentence like "The doctor said they would call tomorrow" becomes "The doctor (masculine) said he would call tomorrow" in Spanish, even though the Finnish or Turkish source contained no gender information. Models apply their priors about occupational gender, and those priors encode the same stereotypes that template probes reveal in monolingual generation.

The consequence is that translation tools actively gender information that was gender-neutral in the source, which is a qualitative change, not a neutral representation of the original. Users relying on translation tools to understand documents in languages they don't speak have no way to know that gender information has been added by the model rather than present in the original.