US Congress House Bill 4355 (Prior Session Legislation)

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Spectrum: Bipartisan Bill
Status: Engrossed on December 10 2019 - 50% progression, died in committee
Action: 2019-12-10 - Received in the Senate and Read twice and referred to the Committee on Commerce, Science, and Transportation.
Pending: Senate Commerce, Science, And Transportation Committee
Text: Latest bill text (Engrossed) [PDF]

(Sec. 3) 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.

IOGAN Act Identifying Outputs of Generative Adversarial Networks Act

Rep. Anthony Gonzalez [R-OH]	Rep. Haley Stevens [D-MI]	Rep. James Baird [R-IN]	Rep. Katie Hill [D-CA]
Rep. Rebecca Sherrill [D-NJ]	Rep. Jennifer Wexton [D-VA]	Rep. Jenniffer Gonzalez-Colon [R-PR]	Rep. Elissa Slotkin [D-MI]
Rep. Frank Lucas [R-OK]	Rep. Abigail Spanberger [D-VA]

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

SB2904 (Related) 2020-12-23 - Became Public Law No: 116-258. (TXT | PDF)

Type	Source
Summary	https://www.congress.gov/bill/116th-congress/house-bill/4355/all-info
Text	https://www.congress.gov/116/bills/hr4355/BILLS-116hr4355ih.pdf
Text	https://www.congress.gov/116/bills/hr4355/BILLS-116hr4355rh.pdf
Text	https://www.congress.gov/116/bills/hr4355/BILLS-116hr4355eh.pdf