This could make chatbots an effective tool for those looking to manipulate public opinion subtly.
In response to these challenges, researchers at the Massachusetts Institute of Technology (MIT) have developed a tool called the Data Provenance Explorer.
Reported by Science Daily on August 30, this tool is designed to help AI practitioners identify data that best suits the purpose of their models, thereby improving accuracy and reducing bias.
Data Provenance Explorer Solution
The Data Provenance Explorer offers machine-learning practitioners the ability to make more informed decisions about the data they use to train their models. This can lead to more accurate models when deployed in real-world applications.
Training large language models requires extensive datasets that combine diverse data from numerous web sources.
However, as these datasets are merged and re-merged into multiple collections, critical information about their origins and usage restrictions can be lost or confused.
This loss of provenance not only raises legal and ethical concerns but can also negatively impact a model’s performance. For example, if a dataset is miscategorized, a practitioner might unknowingly use data inappropriate for the intended task, leading to suboptimal results.
