आतमनरभर भरत —Where are Indigenous Frameworks for Artificial Intelligence?

Abstract

The article discusses the intersection of artificial intelligence (AI) and human resource (HR) analytics, emphasising the need for indigenous frameworks in AI, particularly in the Indian context. It highlights how data has become a critical resource, leading to the rise of HR analytics, which integrates information technology (IT) infrastructure with statistical tools for data-driven decision-making. However, the advent of AI represents a significant disruption, automating decision-making processes and necessitating a re-evaluation of existing frameworks. The authors argue that AI applications often reflect Western epistemologies, perpetuating biases and overlooking indigenous knowledge systems. This ‘Western Universalism’ marginalises diverse cultural perspectives, particularly in management and social sciences. The article critiques the dominance of Western frameworks in knowledge creation and dissemination, suggesting that this has resulted in a lack of representation and innovation in indigenous contexts. The authors also address the ethical implications of AI, particularly algorithmic bias, which can reinforce societal inequalities. They provide examples of biases in contemporary AI tools, such as ChatGPT and Google Bard, which have been found to censor sensitive information or exhibit gender bias in decision-making processes. The article concludes by advocating for developing indigenous AI frameworks that incorporate local knowledge systems and address diverse populations’ unique challenges. This approach aims to create more equitable and inclusive AI applications that benefit a broader segment of society. The authors call for further research into the implications of human-algorithm interactions and the need for accountability in AI practices to mitigate biases and promote fairness in organisational decision-making.

Ähnliche Arbeiten