Guarding Against Misinformation Produced in Generative AI Models

Oregon Convention Center 

The quality of output from Generative AI models is limited by the quality of data it uses to train itself. Input data which is inaccurate, outdated, or incomplete can lead to bad output or hallucinations, where the model confidently asserts that a falsehood is real. We discuss challenges in the estimation of Generative AI models which can cause misinformation including inheriting biases present in the training data and producing outputs that are plausible but fundamentally incorrect or nonsensical. We also discuss mitigation strategies such as the curation of training data, meticulous algorithm design, and continuous monitoring to minimize biases. Additionally, we present an illustrative example on establishing mechanisms for rigorous model evaluation and quality control.