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 TRANSLATE with x EnglishArabicHebrewPolishBulgarianHindiPortugueseCatalanHmong DawRomanianChinese SimplifiedHungarianRussianChinese TraditionalIndonesianSlovakCzechItalianSlovenianDanishJapaneseSpanishDutchKlingonSwedishEnglishKoreanThaiEstonianLatvianTurkishFinnishLithuanianUkrainianFrenchMalayUrduGermanMalteseVietnameseGreekNorwegianWelshHaitian CreolePersian //  TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster PortalBack//

本文通过基于研究的分析，探讨了Meta-AI在WhatsApp平台上面临的社会语言学挑战，重点考察其在适配词汇体系与跨文化沟通中精准伦理实践方面的局限性。研究揭示了Meta-AI系统无法识别截断式本土言语模式（如将“kenapa”误识别为“enapa”），同时未能理解马鲁古、北马鲁古与东努沙登加拉地区特有的定制化俚语“puki”，这凸显了其在错误识别能力与语境理解层面的根本性局限。研究者通过分析AI与用户的交互截图，探讨了严苛的编程实践如何加剧伦理问题——包括因区域语言定义偏差引发的无意侮辱与潜在仇恨言论滥用。研究人员建议，算法应配备包含本地俚语数据库的自适应词汇库，并增设针对语音偏差识别的错误缓解功能。本文强调，需立即落实此类改进措施，以契合全球伦理标准，并维持多语言环境下的市场稳定性。本分析亦指出，社会语言学能力是构建多语言数字环境中AI发展伦理根基的核心要素。