How accurately do the large language models (LLMs) powering chatbots respond to questions on gender equality and women’s empowerment in Indian agriculture

AI-powered chatbots are emerging as a promising tool to deliver agricultural advice. Such AI-based advisory services can reach large numbers of farmers more cost-effectively than traditional extension services. They can almost instantly provide advice across more remote areas, translate technical terms into plain language, quickly adapt to local needs through feedback from users, and increase access to knowledge for marginalized groups. Yet this technology still faces many obstacles. For instance, users require access to reliable electricity and (mobile) internet, which are still unavailable in many of the world's rural farming areas.

Another important problem is that chatbots risk reinforcing existing gender and social inequalities. The large language models (LLMs) underlying chatbots are trained on huge datasets captured from various internet sources, reflecting an array of cultural and social biases, misinformation, and other issues, and are developed and deployed by tech companies often unfamiliar with those problems or local contexts. If these issues are not accounted for, they can skew chatbot responses. In India, for example, women constitute a significant share of the agricultural workforce but face structural disadvantages in land ownership, information and credit access, and technology. Yet research suggests these inequalities may be reinforced by AI tools that are not designed with the goals of improving equity and justice.