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Public Law116th CongressScience, Technology, Communications
S. 2904Became Law

IOGAN Act

This bill became law

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Law

What it doesSummary public law (Dec 23, 2020)

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.

What just happenedDec 23, 2020

Became Public Law No: 116-258.

Who’s behind it

Sen. Cortez Masto, Catherine [D-NV](D-NV)Sponsor
1 cosponsor1 R
Read on Congress.gov
1cosponsors1committees22actions1related bills11subjects
  1. Dec 23, 2020President

    Became Public Law No: 116-258.

  2. Dec 23, 2020BecameLaw36000

    Became Public Law No: 116-258.

  3. Dec 23, 2020President

    Signed by President.

  4. Dec 23, 2020BecameLaw36000

    Signed by President.

  5. Dec 11, 2020Floor

    Presented to President.

  6. Dec 11, 2020President28000

    Presented to President.

  7. Dec 8, 2020FloorH38310

    Motion to reconsider laid on the table Agreed to without objection.

  8. Dec 8, 2020FloorH37100

    On passage Passed without objection. (text: CR H7022-7023)

  9. Dec 8, 2020Floor8000

    Passed/agreed to in House: On passage Passed without objection.

  10. Dec 8, 2020FloorH30200

    Mr. Tonko asked unanimous consent to take from the Speaker's table and consider.

  11. Dec 8, 2020FloorH30000

    Considered by unanimous consent. (consideration: CR H7022-7023)

  12. Nov 19, 2020FloorH15000

    Held at the desk.

  13. Nov 19, 2020FloorH14000

    Received in the House.

  14. Nov 19, 2020Floor

    Message on Senate action sent to the House.

  15. Nov 18, 2020Floor

    Passed Senate with an amendment by Unanimous Consent. (consideration: CR S7082-7083; text of amendment in the nature of a substitute: CR S7082-7083)

  16. Nov 18, 2020Floor17000

    Passed/agreed to in Senate: Passed Senate with an amendment by Unanimous Consent.(consideration: CR S7082-7083; text of amendment in the nature of a substitute: CR S7082-7083)

  17. Nov 9, 2020Calendars

    Placed on Senate Legislative Calendar under General Orders. Calendar No. 580.

  18. Nov 9, 2020Committee

    Committee on Commerce, Science, and Transportation. Reported by Senator Wicker with an amendment in the nature of a substitute. With written report No. 116-289.

    Commerce, Science, and Transportation Committee
  19. Nov 9, 2020Committee14000

    Committee on Commerce, Science, and Transportation. Reported by Senator Wicker with an amendment in the nature of a substitute. With written report No. 116-289.

    Commerce, Science, and Transportation Committee
  20. May 20, 2020Committee

    Committee on Commerce, Science, and Transportation. Ordered to be reported with an amendment in the nature of a substitute favorably.

    Commerce, Science, and Transportation Committee
  21. Nov 20, 2019IntroReferral

    Read twice and referred to the Committee on Commerce, Science, and Transportation.

    Commerce, Science, and Transportation Committee
  22. Nov 20, 2019IntroReferral10000

    Introduced in Senate

Public LawDec 23, 202049

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.

Passed HouseDec 8, 202053

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.

Passed SenateNov 18, 202055

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.

Reported to SenateNov 9, 202025

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.

Introduced in SenateNov 20, 2019

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.

IOGAN Act — Informed