2_use_case_name: Simulation of the Nation (SimoN) Synthetic Data Engine
3_agency: Department of the Treasury
3_abr: TREAS
4_bureau: Internal Revenue Service
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: The Internal Revenue Service (IRS) Enterprise Services (ES), Enterprise Systems Testing (EST) is the testing authority for Information Technology (IT) and provides enterprise-wide testing solutions and critical support across the enterprise for numerous systems and applications.  

The Synthetic Data Engine, or Simulation of the Nation (SimoN) is designed to generate volumes of synthetic tax data to represent a wide range of use cases, without copying Federal Tax Information (FTI) from production systems is an IRS directive.  SimoN is also able to age data to support multi-year tax code concerns for more comprehensive testing sequences spanning multiple tax years. This will improve the volume, diversity and quality of test data being created and processed in a systemic manner. To date, SimoN has produced over 700,000 unique and valid Individual Master File (IMF) tax returns, and over 50,000 unique and valid Business Master File (BMF) returns for testing. SimoN also supports Social Security Administration (SSA) interconnects and SS4 interconnects which allow the data produced to be fully linked between business and individuals. Over 320 dimensions of socioeconomic data are synthesized and validated against United States (U.S.) Census metrics to ensure a validated and realistic synthetic dataset. 

The SimoN solution consumes publicly available statistical data about businesses and the population of the U.S. This includes tax household composition, income and education levels and other data points. The solution combines statistical data with Artificial Intelligence (AI) and Machine Learning (ML) to generate businesses and simulated households with a variety of tax scenarios. The solution then stores the business and population data in a database.  The data is extracted to files for submission to the test environment systems simulating eFile and SSA feeds with no review of data created.

At no point, is Production data (i.e., Personally Identifiable Information (PII), Federal Taxpayer Information (FTI) or Controlled Unclassified Information (CUI) used as input to the Artificial Intelligence/Machine Language (AI/ML) process of SimoN, nor does the simulated output data feed Production systems. In addition to generating synthetic data, this system is designed to simulate the aging of the population. The aging model includes a variety of socioeconomic events, such as births, deaths, marriages, job changes and address changes. SimoN also has multiple levels of automated testing and data validations to ensure the data produced is usable by IRS test systems and simulates businesses and population.
12_outputs: SimoN is capable of outputting 3 tax years of IMF and BMF returns starting in Tax Year 2022. With each XML Schema Definition (XSD) that is released under the Modernized e-File (MeF), SimoN implements each XSD version and provides full regression support for testing in successive years. Each XSD version has automated tests that run to ensure verifications can be run to reduce the likelihood of AI generated synthetic returns that might have data anomalies. 

Also, to help seed test systems with synthetic individuals and synthetic businesses, SimoN can output the Data Master 1 (DM1) file simulating a feed from Social Security Administration (SSA) and Application for Employer Identification Number file (Form SS-4) containing transcribed form information so that the test system can be initialized with the appropriate backend reference data for validation of IMF and BMF entities, as well as the ability to validate incoming synthetic tax returns.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 4/4/2021
19_date_acq_dev_began: 10/1/2021
20_date_implemented: 1/31/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 2032H5-22-C-00097
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: No
33_agency_data: None, all data used for unsupervised model training is based on public data sets from the Census Bureau.
34_data_docs: Documentation is complete: Documentation exists regarding the maintenance, composition, quality, and intended use of the training and evaluation data, as well as any statistical bias across model features and protected groups.
35_demo_features: Marital Status, Residency Status, Employment Status, Income, Other, Sex/Gender, Age, Socioeconomic Status, Ability Status
35_question_type: SimoN leverages over 100 different census attributes for model training in order to generate a synthetic population.
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: No
42_dev_tools_wait: 6-12 months
43_infra_provisioned: Yes
45_compute_request: No
47_timely_resources: No
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts within the agency.
50_internal_review: Limited documentation for review: Some information regarding the model’s performance on specific benchmarks are available or have been shared with specific stakeholders within the agency.