Ethnic media, which caters to diaspora communities in host nations, serves as a vital platform for these communities to both produce content and access information. Rather than utilizing the language of the host nation, ethnic media delivers news in the language of the immigrant community. For instance, in the USA, Bangla ethnic media presents news in Bangla rather than English. This research delves into the prospective integration of large language models (LLM) and multi-lingual machine translations (MMT) within the ethnic media industry. It centers on the transformative potential of using LLM in MMT in various facets of news translation, searching, and categorization. The paper outlines a theoretical framework elucidating the integration of LLM and MMT into the news searching and translation processes for ethnic media. Additionally, it briefly addresses the potential ethical challenges associated with the incorporation of LLM and MMT in news translation procedures.

Education is a right of all, however, every individual is different than others. Teachers in post-communism era discover inherent individualism to equally train all towards job market of fourth industrial revolution. We can consider scenario of ethnic minority education in academic practices. Ethnic minority group has grown in their own culture and would prefer to be taught in their native way. We have formulated such linguistic anthropology(how people learn)based engagement as semi-supervised problem. Then, we have developed an conditional deep generative adversarial network algorithm namely LA-GAN to classify linguistic ethnographic features in student engagement. Theoretical justification proves the objective, regularization and loss function of our semi-supervised adversarial model. Survey questions are prepared to reach some form of assumptions about z-generation and ethnic minority group, whose learning style, learning approach and preference are our main area of interest.

The general public is requested to help eradicate biases based on race and other underprivileged communities in artificial intelligence (AI) algorithms for healthcare. Health scientists are seeking support to resolve how'minoritized' communities, who are actively deprived because of social constructs, would not see future advantages from using AI in healthcare. The scientists, guided by the University of Birmingham and University Hospitals Birmingham, recently reported in Nature Medicine about the introduction of a consultation on a set of principles that they anticipate will cut biases that are said to be present in AI algorithms. There is increasing proof that certain AI algorithms do not work as well for specific groups of people - mainly those in minoritized racial/ethnic communities. A few of these come from biases in the datasets used to create AI algorithms.

Britain's National Health Service (NHS) is famous for offering free medical treatment to all UK citizens. Despite this, uptake of some services remains low, particularly in certain ethnic demographics. The British government has spent many years trying to reduce these inequalities – and now they are investigating how artificial intelligence (AI) can help bridge the gap. NHSx – the NHS' AI lab and health foundation – has a mission "to ensure NHS patients are amongst the first in the world to benefit from leading AI," and "a responsibility to ensure those technologies don't exacerbate existing health inequalities." As part of these efforts, NHSx has recently identified four AI projects that will benefit from additional investment.