Re: NTIA Docket No. 230407-0093 – Request for Comment on AI Accountability Policy

Thank you for providing the R Street Institute (R Street) with the opportunity to comment in response to the National Telecommunications and Information Administrations (NTIA) request for comment on AI Accountability Policy (Request for Comment).[1] My name is Adam Thierer, and I am a senior fellow with R Street’s Technology and Innovation Policy team. R Street is a nonprofit, nonpartisan public policy research organization. I also recently served as a Commissioner on the U.S. Chamber of Commerce “Commission on Artificial Intelligence Competitiveness, Inclusion, and Innovation,” which released a major report on policy issues surrounding artificial intelligence (AI), machine learning and algorithmic systems.[2]

R Street has published several studies relevant to this proceeding, including a new report on, “Flexible, Pro-Innovation Governance Strategies for Artificial Intelligence.”[3] Pages 27 to 33 of that report discussed strategies to “professionalize” AI ethics and explored how algorithmic audits and impact assessments might play a role in that process. This R Street report has been submitted for the record.  

Before posing questions about the specific issues itemized by the first 29 questions of the Request for Comment, it may be helpful to prioritize the final questions (Questions 30-34) that ask “[w]hat role should government policy have, if any, in the AI accountability ecosystem?”[4] 

Last October, the White House released its “Blueprint for an AI Bill of Rights” (AI Blueprint) and an accompanying list of “Key Actions to Advance Tech Accountability and Protect the Rights of the American Public” (Key Actions).[5] Taken together, the AI Blueprint, Key Actions, and this Request for Comment will play an important role in shaping the innovation culture around computational technologies. Innovation culture refers to the, “attitudes towards innovation, technology, exchange of knowledge, entrepreneurial activities, business, uncertainty,” and related activities that determine how a nation treats any particular technology or business sector.[6]

We recommend that the NTIA and the Administration consider the following priorities when formulating AI policy in general or algorithmic audits or impact assessments in particular:

Establish a positive vision and ensure that policy steps do not undermine the potential benefits of algorithmic systems 

Identify barriers to algorithmic innovation, investment and competition

Appreciate the global competitiveness and national security ramifications of AI policy

Acknowledge statutory and constitutional constraints 

Identify how existing state capacity already addresses many concerns

Identify the trade-offs at work with algorithmic transparency and “explainability”

Encourage, but do not force, AI audits and algorithmic impact assessments

We recommend that the NTIA:

  1. Build on the important steps that the National Institute of Standards and Technology (NIST) has taken in its “Artificial Intelligence Risk Management Framework” (AI RMF), which is “designed to address new risks as they emerge.”[27] NIST stressed that the AI RMF is meant to be “risk-based, resource-efficient, pro-innovation, and voluntary.”[28] The AI RMF looks to be “outcome-focused and non-prescriptive … rather than prescribe one-size-fits-all requirements.”[29] The NTIA should work with NIST to build on this consensus-driven approach and resulting voluntary guidance document, which was meant “to offer a resource to the organizations designing, developing, deploying, or using AI systems to help manage the many risks of AI and promote trustworthy and responsible development and use of AI systems.”[30] 
  2. Work with NIST to build on the NTIA’s important past efforts to convene different technology developers and stakeholders and help build consensus around voluntary best practices tailored to unique contexts and concerns. The NTIA and other agencies have brought together diverse stakeholders in the past to find solutions to complicated technology problems as they developed.[31] The same approach can be used to help address algorithmic risk with a standing effort to bring parties together regularly to consider practical response strategies as necessary.
  3. The NTIA, NIST and other federal agencies should also work together to facilitate digital literacy efforts and technology awareness-building efforts, which can help lessen public fears about emerging algorithmic and robotic technologies.[32] Developers have a powerful incentive to build widespread trust in their systems to ensure they get adopted, but they often fail to coordinate with other players to educate the public about the benefits and risks of digital systems. Government can help coordinate and promote more widespread public understanding of these systems and their proper use.

Importantly, impact assessments and audits are just two of many different mechanisms that can help govern algorithmic systems and generate more trust and accountability. Competition and innovation among existing and future market players can also act as a check on developer missteps and provide the public with different platforms and applications to suit specific needs and values. Policymakers should not presume that there is a one-size-fits all approach to algorithmic governance. Moreover, diverse new options can only emerge in an ecosystem free of artificial constraints on entrepreneurial activities. Burdensome mandates would be particular costly to small- and mid-sized firms looking to break into the market and offer alternatives that could provide differing levels of privacy and security protections, for example.

Finally, to reiterate, AI policymaking must not be fear-based or rooted in worst-case thinking. It must, by necessity, be risk based and highly context specific. Generally speaking, the touchstones of wise emerging technology policy are humility, agility, and adaptability, with a strong focus on ongoing communication and collaboration to address fast-moving developments.[33] 

America’s crucial advantage over other countries on the digital technology front has been our uniquely agile and adaptive approach to technological governance.[34] The U.S. policy approach has been rooted in a general freedom to innovate that is accompanied by a diversity of ex-post policy solutions to address problems that develop.[35] This more iterative, bottom-up governance approach not only gives the public more options, but it also provides our nation with a safer and more secure technological base.[36] It has produced the most successful technological revolution of the past half century—with enormous benefits for the economy and consumers.[37] The Administration should look to build on that model and foster the development of trustworthy algorithmic innovations that benefit the public and keep the United States at the forefront of the next great technological revolution.

Respectfully submitted,

____________________________

Adam Thierer

Senior Fellow

R Street Institute

1212 New York Ave. NW,

Suite 900

Washington, D.C. 20005

[email protected]

Footnotes

[1] “AI Accountability Policy Request for Comment,” National Telecommunications and Information Administration, Docket No. 230407-0093, RIN 0660-XC057, April 11, 2023. https://ntia.gov/issues/artificial-intelligence/request-for-comments.

[2]Commission on Artificial Intelligence Competitiveness, Inclusion, and Innovation: Report and Recommendations, U.S. Chamber of Commerce, March 9, 2023. https://www.uschamber.com/technology/artificial-intelligence-commission-report

[3] Adam Thierer, “Flexible, Pro-Innovation Governance Strategies for Artificial Intelligence,” R Street Institute Policy Study No. 283 (April 2023). https://www.rstreet.org/research/flexible-pro-innovation-governance-strategies-for-artificial-intelligence.

[4] “AI Accountability Policy Request for Comment,” p. 29. https://ntia.gov/issues/artificial-intelligence/request-for-comments.

[5] “Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People,” The White House, October 2022. https://www.whitehouse.gov/wp-content/uploads/2022/10/Blueprint-for-an-AI-Bill-of-Rights.pdf; “Fact Sheet: Biden-⁠Harris Administration Announces Key Actions to Advance Tech Accountability and Protect the Rights of the American Public,” The White House, Oct. 4, 2022. https://www.whitehouse.gov/ostp/news-updates/2022/10/04/fact-sheet-biden-harris-administration-announces-key-actions-to-advance-tech-accountability-and-protect-the-rights-of-the-american-public.

[6] Maike Didero et al., “Differences in Innovation Culture Across Europe: A Discussion Paper,” TransForm, February 2008, p. 3. https://www.yumpu.com/en/document/read/6683782/differences-in-innovation-culture-across-europe-transform.

[7] Ibid.

[8] Thierer, “Getting AI Innovation Culture Right,” p. 10. https://www.rstreet.org/research/getting-ai-innovation-culture-right.

[9] Adam Thierer, “Can We Predict the Jobs and Skills Needed for the AI Era?,” R Street Institute Policy Study No. 278 (March 2023), pp. 13-15. https://www.rstreet.org/research/can-we-predict-the-jobs-and-skills-needed-for-the-ai-era.  

[10] “AI Accountability Policy Request for Comment,” p. 22. https://ntia.gov/issues/artificial-intelligence/request-for-comments.

[11] European Commission, “Study supporting the impact assessment of the AI regulation,” European Commission, April 21, 2021, p. 12. https://digital-strategy.ec.europa.eu/en/library/study-supporting-impact-assessment-ai-regulation.

[12] Eric Schmidt, “Innovation Power: Why Technology Will Define the Future of Geopolitics,” Foreign Affairs (March/April 2023). https://www.foreignaffairs.com/united-states/eric-schmidt-innovation-power-technology-geopolitics.

[13] Adam Thierer, “What OpenAI’s Sam Altman Should Say at the Senate AI Hearing,” R Street Institute, May 15, 2023. https://www.rstreet.org/commentary/what-openais-sam-altman-should-say-at-the-senate-ai-hearing.

[14] Adam Thierer, “Statement for the Record on ‘Artificial Intelligence: Risks and Opportunities,’” U.S. Senate Homeland Security and Governmental Affairs Committee, March 8, 2023. https://www.rstreet.org/outreach/testimony-on-artificial-intelligence-risks-and-opportunities.

[15] Thierer, “Getting AI Innovation Culture Right,” p. 6.

[16] NTIA, “Comments of the National Telecommunications and Information Administration Regarding Commercial Surveillance ANPR R11004,” National Telecommunications and Information Administration, Docket FTC-2022-0053, (2022). https://ntia.gov/sites/default/files/publications/ftc_commercial_surveillance_anpr_ntia_comment_final.pdf.

[17] Adam Thierer, “A balanced AI governance vision for America,” The Hill, April 16, 2023. https://thehill.com/opinion/congress-blog/3953916-a-balanced-ai-governance-vision-for-america.

[18] Thierer, “Flexible, Pro-Innovation Governance Strategies for Artificial Intelligence,” pp. 33-36. https://www.rstreet.org/research/flexible-pro-innovation-governance-strategies-for-artificial-intelligence.

[19] John Villasenor, “Products liability law as a way to address AI harms,” Brookings, Oct. 31, 2019. https://www.brookings.edu/research/products-liability-law-as-a-way-to-address-ai-harms.

[22] Thierer, “Flexible, Pro-Innovation Governance Strategies for Artificial Intelligence,” pp. 23-25. https://www.rstreet.org/research/flexible-pro-innovation-governance-strategies-for-artificial-intelligence.

[20] Melanie Mitchell, Artificial Intelligence: A Guide for Thinking Humans (Farrar, Straus and Giroux, 2019), p. 108.

[21] Ibid.

[22] Thierer, “Flexible, Pro-Innovation Governance Strategies for Artificial Intelligence,” pp. 23-25. https://www.rstreet.org/research/flexible-pro-innovation-governance-strategies-for-artificial-intelligence.

[23]Daniel Castro, “Ten Principles for Regulation That Does Not Harm AI Innovation,” Information Technology & Innovation Foundation, Feb. 8, 2023, p. 5. https://itif.org/publications/2023/02/08/ten-principles-for-regulation-that-does-not-harm-ai-innovation.

[24] Thierer, “Flexible, Pro-Innovation Governance Strategies for Artificial Intelligence,” p. 30. https://www.rstreet.org/research/flexible-pro-innovation-governance-strategies-for-artificial-intelligence.

[25] Ellen P. Goodman and Julia Tréehu, “AI Audit-Washing and Accountability,” German Marshall Fund, November 2022, p. 25. https://www.gmfus.org/sites/default/files/2022-11/Goodman%20%26%20Trehu%20-%20Algorithmic%20Auditing%20-%20paper.pdf.

[26] Thierer, “Flexible, Pro-Innovation Governance Strategies for Artificial Intelligence,” pp. 13-23. https://www.rstreet.org/research/flexible-pro-innovation-governance-strategies-for-artificial-intelligence.

[27] National Institute of Standards and Technology, Artificial Intelligence Risk Management Framework (AI RMF 1.0),” U.S. Department of Commerce, January 2023, p. 4. https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf.

[28] Ibid., p. 42.

[29] Ibid.

[30] “NIST Risk Management Framework Aims to Improve Trustworthiness of Artificial Intelligence,” National Institute of Standards and Technology, Jan. 26, 2023, p. 2. https://www.nist.gov/news-events/news/2023/01/nist-risk-management-framework-aims-improve-trustworthiness-artificial

[31] Ryan Hagemann et al., “Soft Law for Hard Problems: The Governance of Emerging Technologies in an Uncertain Future,” Colorado Technology Law Journal 17 (Feb. 5, 2018). https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3118539.

[32] “Commission on Artificial Intelligence Competitiveness, Inclusion, and Innovation: Report and Recommendations,” U.S. Chamber of Commerce Technology Engagement Center, March 9, 2023, pp. 44-45.  https://www.uschamber.com/assets/documents/CTEC_AICommission2023_Report_v6.pdf.

[33] Adam Thierer, “Governing Emerging Technology in an Age of Policy Fragmentation and Disequilibrium,” American Enterprise Institute, April 2022. https://platforms.aei.org/can-the-knowledge-gap-between-regulators-and-innovators-be-narrowed.

[34] Adam Thierer, Permissionless Innovation: The Continuing Case for Comprehensive Technological Freedom, 2nd ed. (Mercatus Center at George Mason University, 2016).

[35] Ibid.

[36] Adam Thierer, “U.S. Chamber AI Commission Report Offers Constructive Path Forward,” R Street Institute, March 9, 2023. https://www.rstreet.org/commentary/u-s-chamber-ai-commission-report-offers-constructive-path-forward.

[37] Tina Highfill and Christopher Surfield, “New and Revised Statistics of the U.S. Digital Economy, 2005–2021,” Bureau of Economic Analysis, November 2022. https://www.bea.gov/system/files/2022-11/new-and-revised-statistics-of-the-us-digital-economy-2005-2021.pdf.