The Sociolinguistic Experience of Meta-AI’s Vernacular Modification in Eastern Indonesian Settings Reveals Lexical Obstacles and Ethical Puzzles

ÛThis article examines Meta-AI's sociolinguistic challenges on WhatsApp through research-based analysis of its limitations in adapting lexicon and precise ethical practices in intercultural communication. The study demonstrates how Meta-AI system fails to read truncated vernacular speech patterns (“kenapa” → “enapa”) while missing customized slang (“puki”) used specifically in Maluku, North Maluku and East Nusa Tenggara regions to show fundamental limitations in error recognition capabilities and contextual understanding. The authors analyze AI-user screenshots to understand how strict programming practices increase ethical problems including unintentional insult and potential hate speech abuse through regional language definitions. Researchers recommend that algorithms should have adaptive lexicons containing local slang databases which also include error-mitigation features for phonetic deviation recognition. The article stresses that these required improvements must be implemented immediately to satisfy worldwide ethical standards and to maintain the market stability within multilingual environments. The analysis builds up sociolinguistic competence as a vital fundamental aspect that builds the ethical foundations for AI advancement throughout the plurilingual digital environment.ª

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Arabic	Hebrew	Polish
Bulgarian	Hindi	Portuguese
Catalan	Hmong Daw	Romanian
Chinese Simplified	Hungarian	Russian
Chinese Traditional	Indonesian	Slovak
Czech	Italian	Slovenian
Danish	Japanese	Spanish
Dutch	Klingon	Swedish
English	Korean	Thai
Estonian	Latvian	Turkish
Finnish	Lithuanian	Ukrainian
French	Malay	Urdu
German	Maltese	Vietnamese
Greek	Norwegian	Welsh
Haitian Creole	Persian

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