Inferential RC

The advent of artificial intelligence in agriculture has fundamentally altered crop management practices. Farmers now employ predictive analytics to forecast pest infestations and disease outbreaks months in advance. By analysing weather patterns, soil composition, and historical data, AI systems enable early intervention strategies that significantly reduce crop loss. However, widespread adoption remains limited in developing nations due to high infrastructure costs and limited technical expertise. Consequently, the technology's potential to address global food security remains largely unrealised in regions where it is most needed. Question: What can be inferred about the relationship between technological advancement and food security in developing nations?

Artificial intelligence systems trained on historical data inevitably encode the biases present in that data. When algorithms are used to assess loan applications, predict criminal recidivism, or recommend job candidates, these encoded biases translate into discriminatory outcomes that disproportionately affect marginalised communities. Developers often claim that removing sensitive variables like race or gender solves the problem, but this approach is naive: proxy variables—such as zip code or educational background—can reproduce the same discriminatory patterns. True fairness in AI requires not just technical fixes, but a fundamental reimagining of how we collect, label, and validate training data, alongside ongoing human oversight. Question: What does the passage imply about the limitations of removing sensitive variables from AI training data?

The Green Revolution of the 1960s transformed agricultural productivity across South Asia by introducing high-yield crop varieties and synthetic fertilisers. However, decades of intensive farming depleted soil nutrients and contaminated groundwater with chemical residues. Farmers now face diminishing returns despite increased input costs. Agricultural scientists argue that sustainable practices—crop rotation, organic composting, and precision irrigation—offer long-term viability, though adoption remains slow due to initial investment barriers and entrenched conventional methods. Question: What can be inferred about the relationship between the Green Revolution's initial success and current agricultural challenges?

Microplastics—tiny plastic particles smaller than 5 millimetres—have infiltrated every ecosystem on Earth, from deep ocean trenches to Arctic ice. These fragments originate from the breakdown of larger plastic waste and the direct release of microbeads from cosmetics and textiles. Marine organisms ingest microplastics, mistaking them for food, which accumulates in their tissues. Scientists are uncertain whether bioaccumulation in the food chain poses direct toxicity to humans, though the presence of microplastics in human blood and lungs has been recently documented. Question: Why does the passage describe microplastics as having 'infiltrated' rather than simply 'spread to' every ecosystem?