Collaborative Culture-Sensitive AI
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As a way to counteract online western-centric monopolization, algorithmic structures, collaboratively trained with inputs derived from minority communities, work as a means to preserve traditional cultural heritage, endangered languages, and customs. By adding culturally-diverse imagery to deep learning structures, the outputs can generate more plural content. As it is collaborative, individuals worldwide work together to build a digital environment that widens up the exchange between different cultures, thus putting emphasis on minority groups that are often denied visibility.
Through natural language processing (NLP), the same collaborative structure is fed by different speech varieties, thus helping machines to get acquainted with different vernacular languages besides English. In short, the fusion of artificial intelligence with cultural preservation can help diminish bias in algorithmic identification, while training software to be more inclusive.