The suitability of different data types for use in generative artificial intelligence models is a critical consideration. Data types that minimize the risk of bias amplification, privacy violations, and the generation of harmful content are considered safer. For example, carefully curated, anonymized datasets of numerical data for predicting trends carry less risk than unverified text datasets scraped from the internet.
Selecting appropriate data inputs is paramount for ethical and responsible AI development. It reduces the potential for models to perpetuate societal biases present in the training data, prevents the unintentional disclosure of sensitive personal information, and lessens the likelihood of the AI producing outputs that are discriminatory, offensive, or misleading. A thoughtful approach to data selection contributes to building trustworthy and beneficial AI systems.
