Working Together AP-1.2

Protect Our Cultures

AI should celebrate our differences, not flatten them.

Without active correction, AI output drifts toward a dominant style: dominant language, dominant aesthetics, dominant perspective. AP-1.2 sets the counter-rule: preserve diversity instead of flattening it. ¹ ²

What This Means

This policy means AI should not flatten people into one global style. Language variation, regional context, and cultural memory are part of quality, not optional extras. AI is truly useful only when it can stay accurate without erasing local identity.

A Real-World Scenario

A student in a bilingual region asks AI for school material that mixes standard language with local terms. Today, systems often normalize everything to one dominant variant and remove local references. With AP-1.2, the model would preserve that language mix, keep regional context, and provide culturally fitting examples instead of generic mainstream output.

Why It Matters to You

When AI smooths away cultural difference, outputs may look fluent but still be wrong for real users. That creates digital invisibility for communities underrepresented in dominant datasets. AP-1.2 reframes quality as contextual relevance, not just grammatical polish. ¹ ³

If We Do Nothing...

If we do nothing, each model generation reinforces the same dominant perspective. As more capable systems approach AGI-like influence, that homogenization scales automatically because it is operationally convenient. AP-1.2 keeps diversity as a default behavior, not an exception. ¹ ⁴

For the technically inclined

AP-1.2: Cultural Diversity

AI systems should preserve and promote cultural diversity rather than homogenize cultural expression, language, or creative output.

What You Can Do

Review AI outputs for context fit, not just grammar. Ask: are examples culturally relevant, and are local terms handled correctly? Report recurring flattening and decontextualization.

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