IOGAN Act 2020-12-23 Public Law Identifying Outputs of Generative Adversarial Networks Act or the IOGAN Act

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).

Specifically, the NSF must support research on manipulated or synthesized content and information authenticity and the NIST must support research for the development of measurements and standards necessary to accelerate the development of the technological tools to examine the function and outputs of generative adversarial networks or other technologies that synthesize or manipulate content.

]]> 2020-12-08 Passed House Identifying Outputs of Generative Adversarial Networks Act or the IOGAN Act

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).

Specifically, the NSF must support research on manipulated or synthesized content and information authenticity and the NIST must support research for the development of measurements and standards necessary to accelerate the development of the technological tools to examine the function and outputs of generative adversarial networks or other technologies that synthesize or manipulate content.

]]> 2020-11-18 Passed Senate Identifying Outputs of Generative Adversarial Networks Act or the IOGAN Act

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).

Specifically, the NSF must support research on manipulated or synthesized content and information authenticity and the NIST must support research for the development of measurements and standards necessary to accelerate the development of the technological tools to examine the function and outputs of generative adversarial networks or other technologies that synthesize or manipulate content.

]]> 2020-11-09 Reported to Senate Identifying Outputs of Generative Adversarial Networks Act or the IOGAN Act

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).

Specifically, the NSF must support research on manipulated or synthesized content and information authenticity and the NIST must support research for the development of measurements and standards necessary to accelerate the development of the technological tools to examine the function and outputs of generative adversarial networks or other technologies that synthesize or manipulate content.

]]> 2019-11-20 Introduced in Senate Identifying Outputs of Generative Adversarial Networks Act or the IOGAN Act

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).

Specifically, the NSF must support research on manipulated or synthesized content and information authenticity and NIST must support research for the development of measurements and standards necessary to accelerate the development of the technological tools to examine the function and outputs of generative adversarial networks or other technologies that synthesize or manipulate content.

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