Many efforts, however, miss an important fact: ethics differ from one cultural context to the next...
Western perspectives are also implicitly being encoded into AI models. For example, some estimates show that less than 3% of all images on ImageNet represent the Indian and Chinese diaspora, which collectively account for a third of the global population. Broadly, a lack of high-quality data will likely lead to low predictive power and bias against underrepresented groups — or even make it impossible for tools to be developed for certain communities at all. LLMs can’t currently be trained for languages that aren’t heavily represented on the Internet, for instance. A recent survey of IT organizations in India revealed that the lack of high-quality data remains the most dominant impediment to ethical AI practices.
As AI gains ground and dictates business operations, an unchecked lack of variety in ethical considerations may harm companies and their customers.
To address this problem, companies need to develop a contextual global AI ethics model that prioritizes collaboration with local teams and stakeholders and devolves decision-making authority to those local teams. This is particularly necessary if their operations span several geographies."
