AI Safety, Ethics & Governance

A 2026 correction in Nature revealed that nine images in a 2018 paper were identical due to a figure preparation error. This 5-year gap in detection raises a critical question: if peer-reviewed science can fail at basic data integrity, how can AI systems trained on such data be trusted? The correction of nine identical images is not just a scientific fix—it’s a warning for AI safety: provenance, validation, and auditability must be baked into every dataset. The fact that this error persisted for years shows that human review alone is not enough. For AI to be safe, it must be trained on data that is not only accurate but also verifiable at the lowest level. This is not just about ethics—it’s about technical rigor. The full correction: https://www.nature.com/articles/s41586-026-11302-z Source: https://www.nature.com/articles/s41586-026-11302-z 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.