“Garbage in, garbage out” still defines how AI performs in the real world. When AI makes mistakes, the problem often stems from the quality of training data. Flawed inputs lead to biased outputs, unsafe model behavior and costly mistakes.
With regulators mandating rigorous testing and safety audits, developers and deployers must treat data like a core technology — designed, tested and maintained with intent.
Inside the paper:
- 3 “data traps” undermining AI safety and performance
- How to build and evaluate data pipelines
- Where specialized partners accelerate secure AI development
