More on Oversight
Today, the U.S. House of Representatives considered and passed five bipartisan Science, Space, and Technology Committee bills under suspension of the rules.
This bill directs the National Science Foundation (NSF) and the National Institute of Standards and Technology (NIST) to support research on generative adversarial networks. A generative adversarial network is a software system designed to be trained with authentic inputs (e.g., photographs) to generate similar, but artificial, outputs (e.g., deepfakes).
Bloomfield Public Schools Early Childhood Center at Forest Glen
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Good Afternoon and thank you, Chairwoman Sherrill, for convening this important hearing.
We are here today to explore technologies that enable online disinformation. We’ll look at trends and emerging technology in this field, and consider research strategies that can help to detect and combat sophisticated deceptions and so-called “deepfakes.”
Disinformation is not new. It has been used throughout history to influence and mislead people.
In science, carrying out our work with integrity is a bedrock principle.
To quote a National Academies report on the responsible conduct of research: “The public will support science only if it can trust the scientists and institutions that conduct research.”
We must have rigorous policies on scientific integrity, research misconduct, conflict of interest, and data transparency. This instills public trust and confidence in taxpayer funded research.