A Vision for an Integrated Data Infrastructure Using Artificial Intelligence to Analyze Household Income, Consumption, and Wealth in US Federal Law
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RIS BIB ENDNOTEPublication date: 12.06.2026
Financial Law Review, 2026, Issue 41 (1)/2026, pp. 47-62
https://doi.org/10.4467/22996834FLR.26.003.23697Authors
A Vision for an Integrated Data Infrastructure Using Artificial Intelligence to Analyze Household Income, Consumption, and Wealth in US Federal Law
This article presents a vision for an integrated data infrastructure powered by artificial intelligence (AI) in the United States, enabling precise analysis of household income, consumption, and wealth. Faced with declining survey response rates, rising costs, and the limitations of a fragmented federal statistical system, the analysis encompasses three reports from the National Academies of Sciences, Engineering, and Medicine (2023–2024) and current federal law (including the Evidence Act and the Federal Data Strategy). The main hypothesis is that integrating administrative, survey, and potentially private data sources using AI will enable more accurate, timely, and consistent well-being measurements and ongoing assessments of the impact of economic policies on citizens’ living standards. The study is based on a content analysis of the reports and a holistic review of legal regulations concerning data management and AI in the federal government. The study determined that the proposed infrastructure could overcome existing problems (underestimation, inconsistent definitions, and poor timeliness) by creating uniform measures based on national accounts, enabling precise determination of income sources and wealth structure, and assessing the effects of redistribution, pension provision, and intergenerational transfers. The conclusions point to the groundbreaking potential of the implemented neural data management model using AI, which is expected to increase the accuracy, timeliness, and usefulness of statistics while maintaining privacy protection. The originality of the article lies in its synthesis of recent academic reports with legal analysis and the introduction of the concept of household “budget identity”.
Bean R.: How the Pentagon Chief Data Officer and his unit are taking the beach, Updated June 16, Forbes, 2021.
Gale W., Vignaux S.: The difference between how the wealthy make money—and pay taxes, The Brookings Institution, September 7th, 2023.
Groves R., Mesenbourg T., Siri M. (eds.): Toward a 21st Century National Data Infrastructure: Mobilizing Information for the Common Good, National Academies of Sciences, Engineering and Medicine 2023.
Hart N., Stefanik S.: Evidence Commission after 5 years: A Progress Report on the Promise for a More Evidence-Informed Society, Data Foundation 2022.
Iacoviello M.: Housing Wealth and Consumption, Board of Governors of the Federal Reserve System, International Finance Discussion Papers, Number 1027, August 2011.
Knoedl T. R.: The U.S. Census Bureau: An Overview, Congressional Research Service, R47847, November 22, 2023.
Lizak R.: Prawne aspekty neuronowego modelu zarządzania danymi w administracji federalnej Stanów Zjednoczonych z wykorzystaniem AI [Legal aspects of a neural network-based data management model in the US federal administration with the use of AI], C.H. Beck, Warsaw: 2024.
Lizak R., Skuza S.: Sztuczna inteligencja umożliwia kontrolę finansów publicznych – przegląd inicjatyw amerykańskiego rządu federalnego [Artificial intelligence enables the monitoring of public finances – an overview of US federal government initiatives], Białostockie Studia Prawnicze [Białystok Legal Studies], Vol. 28 nr 2, 2023.
Lohr S.L, D.H. Weinberg, Marton K. (eds.): Toward a 21st Century National Data Infrastructure: Enhancing Survey Programs by Using Multiple Data Sources, National Academies of Sciences, Engineering and Medicine 2023.
Schmidt E.: Final Report. National Security Commission on Artificial Intelligence, March 1, 2021.
Smeeding T.M., Johnson D.S, Citro C.F. (eds.): Creating an Integrated System of Data and Statistics on Household Income, Consumption, and Wealth: Time to Build, National Academies of Sciences, Engineering, and Medicine 2024.
Stiglitz J., Fitoussi J., Durand M.: Beyond GDP: Measuring that counts for economic and social performance, OECD Publishing, 2018.
Taylor P.: Leading countries by number of data centers as of March 2025, March 21st, 2025. Available at: https://www.statista.com/statistics/1228433/data-centers-worldwide-by-country/, accessed: January 1st, 2026.
Tierney A.: Total number of government employees in the United States from 1982 to 2023, March 27th, 2025. Available at: https://www.statista.com/statistics/204535/number-of-governmental-employees-in-the-us/, accessed: January 1st, 2026.
Wiseman J.M.: Data-Driven Government: The Role of Chief Data Officers, IBM Center for The Business of Government, Ash Center for Democratic Governance and Innovation, Harvard Kennedy School, 2018.
Advancing American AI Act, 117th Congress, Public Law 117-270, S.1353, December 19, 2022.
AI in Government Act of 2020, H.R.2575, 116th Congress, September 14, 2020.
Confidential Information Protection and Statistical Efficiency Act, (CIPSEA), the 107th U.S. Congress, Public Law 107–347, 116 Stat. 2899, 44 U.S.C. § 101, December 17, 2002.
Executive Order 13859 of February 11, 2019, Maintaining American Leadership in Artificial Intelligence.
Executive Order 13960 of December 3, 2020, Promoting the Use of Trustworthy Artificial Intelligence in the Federal Government.
Foundations for Evidence-Based Policymaking Act, (Evidence Act), the 115th U.S. Congress, Public Law 115-435, January 14, 2019.
National AI Research Resource Task Force Act of 2020, 116th Congress, Public Law 116-283, S.3890, January 1, 2021.
National Artificial Intelligence Initiative Act of 2020, H.R.6216, 116th Congress, December 03, 2020.
Board of Governors of the Federal Reserve System, Survey of Consumer Finances (SCF), 2022. Available at: https://www.federalreserve.gov/econres/scfindex.htm, accessed: 18th April, 2025
Federal Data Strategy 2020 Action Plan, President’s Management Agenda, 2020. Available at: https://strategy.data.gov/assets/docs/2020-federal-data-strategy-action-plan.pdf, accessed: 18th April, 2025.
Federal Data Strategy. 2021 Action Plan, Available at: https://strategy.data.gov/assets/docs/2021-Federal-Data-Strategy-Action-Plan.pdf, accessed: April 18th, 2025.
National Academies of Sciences, Engineering, and Medicine. 2023a. Toward a 21st Century National Data Infrastructure: Mobilizing Information for the Common Good. Washington, DC: The National Academies Press. Available at: https://doi.org/10.17226/26688, accessed: April 18th, 2025.
National Academies of Sciences, Engineering, and Medicine. 2023b. Toward a 21st Century National Data Infrastructure: Enhancing Survey Programs by Using Multiple Data Sources. Washington, DC: The National Academies Press, Available at: https://doi.org/10.17226/26804, accessed: 18th April, 2025.
National Academies of Sciences, Engineering, and Medicine. 2024. Creating an Integrated System of Data and Statistics on Household Income, Consumption, and Wealth: Time to Build. Washington, DC: The National Academies Press, https://doi.org/10.17226/27333, accessed: 18th April, 2025.
The Federal Statistical System: An Overview, Congressional Research Service, R48161, August 19, 2024, Available at: https://www.congress.gov/crs-product/R48161, accessed: 18th April, 2025.
The Home of the U.S. Government’s Open Data, https://data.gov, accessed: 18th April, 2025.
United States Census Beaureau, Current Population Survey (CPS). Available at: https://www.census.gov/programs-surveys/cps.html, accessed: 18th April, 2025
Information: Financial Law Review, 2026, Issue 41 (1)/2026, pp. 47-62
Article type: Original scientific article
Published at: 12.06.2026
Received at: 01.01.2026
Accepted at: 27.05.2026
Article status: Open
Licence: CC BY-NC-ND 4.0
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EnglishView count: 83
Number of downloads: 45
