IOGAN Act

Bill: 116-hr-4355
Sponsor: Rep. Gonzalez, Anthony [R-OH-16]
Introduced: 2019-09-17 in the House
Policy area: Science, Technology, Communications
Subjects: Advanced technology and technological innovations
Computers and information technology
Congressional oversight
Digital media
Government studies and investigations
Intergovernmental relations
Photography and imaging
Research administration and funding
Research and development
Research ethics
Technology assessment

Summary:
action_date: 2019-09-17
text: 

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 the science and ethics of material produced by generative adversarial networks and NIST must support research to accelerate the development of tools to examine the function and outputs of generative adversarial networks.

action_date: 2019-11-05
text: 

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 manipulated or synthesized media, including the output of 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.

action_date: 2019-12-09
text: 

Identifying Outputs of Generative Adversarial Networks Act or the IOGAN Act

 
(Sec. 3) This bill directs the National Science Foundation (NSF) and the National Institute of Standards and Technology (NIST) to support research on manipulated or synthesized media, including the output of 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.
 
 (Sec. 4) 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.
 
NIST shall conduct outreach to (1) receive input from private, public, and academic stakeholders on fundamental measurements and standards research necessary to examine the function and outputs of generative adversarial networks; and (2) consider the feasibility of an ongoing public and private sector engagement to develop voluntary standards for the function and outputs of such networks or other technologies that synthesize or manipulate content.
 
(Sec. 5) The NSF and NIST must jointly submit to Congress a report containing (1) such agencies' findings with respect to the feasibility for research opportunities with the private sector, including digital media companies to detect the function and outputs of generative adversarial networks or other technologies that synthesize or manipulate content; and (2) any policy recommendations of those agencies that could facilitate and improve communication and coordination between the private sector, the NSF, and relevant federal agencies through the implementation of innovative approaches to detect digital content produced by such networks or such technologies. 


Legislative actions:
action_date: 2019-12-10
text: Received in the Senate and Read twice and referred to the Committee on Commerce, Science, and Transportation.
action_date: 2019-12-09
text: Motion to reconsider laid on the table Agreed to without objection.
action_date: 2019-12-09
text: On motion to suspend the rules and pass the bill, as amended Agreed to by voice vote. (text: CR H9363-9364)
action_date: 2019-12-09
text: Passed/agreed to in House: On motion to suspend the rules and pass the bill, as amended Agreed to by voice vote.(text: CR H9363-9364)
action_date: 2019-12-09
text: DEBATE - The House proceeded with forty minutes of debate on H.R. 4355.
action_date: 2019-12-09
text: Considered under suspension of the rules. (consideration: CR H9363-9364)
action_date: 2019-12-09
text: Ms. Johnson (TX) moved to suspend the rules and pass the bill, as amended.
action_date: 2019-11-05
text: Placed on the Union Calendar, Calendar No. 213.
action_date: 2019-11-05
text: Reported (Amended) by the Committee on Science, Space, and Technology. H. Rept. 116-268.
action_date: 2019-11-05
text: Reported (Amended) by the Committee on Science, Space, and Technology. H. Rept. 116-268.
action_date: 2019-09-25
text: Ordered to be Reported (Amended) by Voice Vote.
action_date: 2019-09-25
text: Committee Consideration and Mark-up Session Held.
action_date: 2019-09-17
text: Referred to the House Committee on Science, Space, and Technology.
action_date: 2019-09-17
text: Introduced in House
action_date: 2019-09-17
text: Introduced in House