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Contribution short abstract

Inequalities in uses, access and reach of AI in South Asian society is hard to ignore. If we have to understand these inequalities related to AI, then we need to explore it on the basis of case study.. This study is trying to do the same.

Contribution long abstract

AI’s real value lies in its collaboration with human expertise, complementing rather than replacing human effort. This ties to another myth about AI being unbiased (Nussbaum, 2023). In reality, AI's fairness and productivity depends on the data and algorithms behind it. An example of this is the growing research on gender bias in machine translation systems like Google Translate.(Farkas & Németh, 2022)

The need for deeper exploration of AI's capabilities and limitations is often driven by tech enthusiasts, predominantly males, who spend more time engaging with AI technologies (Humlum & Vestergaard, 2024). On the other hand, females tend to use AI more frequently but exhibit greater concerns (Bouzar et al., 2024).

These concerns are often rooted more in fear of the unknown than in factual experiences, but there is a cost to this.

All predictive models, AI included, are more accurate when they incorporate diverse human experiences, much in line with the Diversity prediction theorem. The fact that AI is growing at such a pace while leaving behind many under-representative groups, because of these apprehensions, is dangerous for the society, as disparities like these could exacerbate economic inequality among diverse groups. (Aldasoro et al., 2024)

Underscoring the need for data-driven analysis and anecdotal research to clarify AI’s true capabilities and limitations, this study will be an attempt at showing what are the costs of AI moving forward with this racial and gender gap by using Case Study method.