AI Safety, Ethics & Governance

One copy-paste error in a 2019 Nature paper — corrected only in 2026 — exposes a critical flaw in scientific trust. When even peer-reviewed research is vulnerable to simple data corruption, how can AI systems trained on such literature be considered safe? This correction in the study on histone H3 trimethylation and m6A RNA modification reveals a deeper issue: reproducibility is not guaranteed by publication. For AI safety, this means we must treat data provenance and replication as non-negotiable — not just for models, but for the research that trains them. · Published November 09, 2026 https://www.nature.com/articles/s41586-026-11375-w The lesson for AI safety? Trust doesn't come from publication — it comes from verifiable, repeatable science. Source: https://www.nature.com/articles/s41586-026-11375-w View and join any or all of our communities — AI, quantum, and real estate: https://alex-companies.com/reports/linkedin-groups If there's an AI, quantum, or real estate community you'd like to see us convene, tell us — this portfolio grows by request.