Researchers developed an easy-to-use tool that enables an AI practitioner to find data that suits the purpose of their model, which could improve accuracy and reduce bias.
In order to train more powerful large language models, researchers use vast dataset collections that blend diverse data from thousands of web sources.
But as these datasets are combined and recombined into multiple collections, important information about their origins and restrictions on how they can be used are often lost or confounded in the shuffle.
Not only does this raise legal and ethical concerns, it can also damage a model’s performance.
“These types of tools can help regulators and practitioners make informed decisions about AI deployment, and further the responsible development of AI,” says Alex “Sandy” Pentland, an MIT professor, leader of the Human Dynamics Group in the MIT Media Lab, Boston Global Forum and AI World Society Board Member, and co-author of a new open-access paper about the project.
Please read the full article on MIT News.
