2_use_case_name: Automated Realtime Global Organization Specialist (ARGOS) for Company Registration Submissions to E-Verify
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: USCIS
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: ARGOS sentiment analysis produces a risk score and keyword extraction identifies the keword category of interest to the VAC MPAs (management and program analyst) for the aggregated open-source information to help quickly identify any pertinent information to aid the MPAs in their open-source investigation of company applications.  This saves potentially thousands of MPA man hours in open-source investigation and creates a single source-of-truth for each MPAs investigation of a company application.  This, in turn, allows for quicker application processing and, if risk of company fraud exists, much faster referral processing time quickening the next-step referral to FDNS for further investigations.
12_outputs: Responses back to a user dashboard accessible internally only by VAC Management and Program Analyst (MPA) personnel.  Keywords relating to the MPA's work interest are extracted if present and risk scores are assigned to the open-source collected information. The data is presented to the MPA on the GUI (graphical user interface) dashboard.
16_dev_stage: Operation and Maintenance
17_impact_type: Rights-Impacting

18_date_initiated: 6/16/2021
19_date_acq_dev_began: 6/16/2021
20_date_implemented: 8/12/2023
22_dev_method: Developed with contracting resources.
23_contract_piids: 70SBUR24F00000163
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Other
31_question_type: The machine learning models are using publicly safe open-source data. They do not need agency data for training. The process utilized for ARGOS is transfer learning.
33_agency_data: The fine-tuned dataset is collected from open-source queries from the Bing API connected to the ARGOS system.  This is publicly available data that doesn't contain any PII.
34_data_docs: Documentation has been partially completed: Some documentation exists (detailing the composition and any statistical bias or measurement skew for training and evaluation purposes), but documentation took place within this use case’s development.
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: Yes
41_system_name: ARGOS (Automated Real-Time Global Organization Specialist)
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: No
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
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.
51_extension_request: No – Agency did not request an extension for this use case.
52_impact_assessment: Yes
53_real_world_testing: Impact evaluation in operational environment: The AI use case has been tested in an operational environment before being fully implemented as a solution and has utilized randomized experiments with a control group or other counterfactual, social systems analysis, or other rigorous research methodologies to evaluate impact and identify potential harm to users as well as broader groups of people.
54_key_risks: Data Collection Bias: search engine prioritization of certain content may skew results. Lack of Domain-Specific Accuracy: model tested on company data across different industries resulted in inconsistent performance. Limited Generalization to Unseen Data: model’s performance on validation datasets lower than on training data, indicating potential overfitting. Misinterpretation of Sentiment: instances of sarcasm/irony not recognized. All risks identified in testing and evaluation phases. 
55_independent_eval: Yes – by the CAIO
56_monitor_postdeploy: Intermittent and Manually Updated: A plan for monitoring the AI use case is in place, and requires data science teams to work with DevOps engineers to manually update models at scheduled intervals, and create metrics to detect data distribution shifts between the operational environment and the training data for the model. 
57_autonomous_impact: No - Some individual decisions or actions require direct human oversight.
59_ai_notice: N/A - individuals are not interacting with the AI for this use case
61_adverse_impact: No
62_disparity_mitigation: Bias monitoring, fine-tuning data balancing, and statistical precision deviation monitoring have been put in place.
63_stakeholder_consult: None of the above
65_appeal_process: Yes
67_opt_out: No – This AI use case is not subject to the opt-out requirement because the AI functionality is solely used for the prevention, detection, and investigation of fraud or cybersecurity incidents, or the conduct of a criminal investigation.