Language Processing in AI and Its Impact on Human Identity and Culture

With an emphasis on ethics, this paper looks at the evolution of AI and how it has changed the understanding of how human identity and the human culture will be reflected in the language processing models. From early rule based systems all the way to state of the art deep learning architectures such as GPT-3, BERT, and T5, developed AI language models have completely changed the way humans and computers interact. For example, GPT-3 can get 95 percect accuracy in text generation tasks, BERT and T5 likewise show their excellent context understanding accurately at 90% and 92% respectively. However, there are concerns about cultural homogenization when these models are applied widely; AI systems primarily favor the language English and dedicate 60 percent of the AI training data to it. This fifteen (15) percent drop in performance has meant that for models that process African American Vernacular English (AAVE) and other lesser Western languages, this 25% drop in performance is too large a price to pay. The research also identifies three key ethical issues of algorithmic bias, data privacy, and the veracity of AI generated content. Biased data set trained AI systems continue to leverage these biases, and perpetuate the same societal inequalities. Though these challenges, AI language models have also stood as a bridge for transcending cultural communication with the help of Google Translate or any such tool providing real time language translation. The conclusion of this paper also calls for future models of AI to promote inclusivity as well as mitigate bias and preserve linguistic diversity. For fair and diverse communication in the digital age, it is imperative to develop and culturally inclusively ensure that AI systems are ethically developed.