Dominic Sobhani

Daniel Björkegren, Jun Ho Choi, Divya Budihal et al.

Although 85% of sub-Saharan Africa's population is covered by mobile broadband signal, only 37% use the internet, and those who do seldom use the web. The most frequently cited reason for low internet usage is the cost of data. We investigate whether AI can bridge this gap by analyzing 40,350 queries submitted to an AI chatbot by 469 teachers in Sierra Leone over 17 months. Teachers use AI for teaching assistance more frequently than web search. We compare the AI responses to the corresponding top search results for the same queries from the most popular local web search engine, google.com.sl. Only 2% of results for corresponding web searches contain content from in country. Additionally, the average web search result consumes 3,107 times more data than an AI response. Bandwidth alone costs \$2.41 per thousand web search results loaded, while the total cost of AI is \$0.30 per thousand responses. As a result, AI is 87% less expensive than web search. In blinded evaluations, an independent sample of teachers rate AI responses as more relevant, helpful, and correct than web search results. These findings suggest that AI-driven solutions can cost-effectively bridge information gaps in low-connectivity regions.

Dominic Sobhani, Amir Feder, David Blei

Probabilistic topic models are a powerful tool for extracting latent themes from large text datasets. In many text datasets, we also observe per-document covariates (e.g., source, style, political affiliation) that act as environments that modulate a "global" (environment-agnostic) topic representation. Accurately learning these representations is important for prediction on new documents in unseen environments and for estimating the causal effect of topics on real-world outcomes. To this end, we introduce the Multi-environment Topic Model (MTM), an unsupervised probabilistic model that separates global and environment-specific terms. Through experimentation on various political content, from ads to tweets and speeches, we show that the MTM produces interpretable global topics with distinct environment-specific words. On multi-environment data, the MTM outperforms strong baselines in and out-of-distribution. It also enables the discovery of accurate causal effects.