2_use_case_name: City Pairs Program Ticket Forecast and Scenario Analysis Tools
3_agency: General Services Administration
3_abr: GSA
4_bureau: FAS
8_topic_area: Transportation
10_commercial_ai: None of the above.
11_purpose_benefits: Takes segment-level City Pair Program air travel purchase data and creates near-term forecasts for the current and upcoming fiscal year by month and at various levels of granularity including DOD vs Civilian, Agency, and Region.
12_outputs: City Pairs Program Ticket Forecast and Scenario Analysis Tools
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 10/1/2021
19_date_acq_dev_began: 2/1/2022
20_date_implemented: 4/1/2019
22_dev_method: Developed in-house.
24_hisp_support: No
25_hisp_name: Public Experience Portfolio – GSA 
26_public_service: Enable increased self-service through an expansion of digital costumer service tools
27_public_info: No
28_iqa_compliance: Non-public
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Metadata Link
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. 
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
39_code_link: It is available on the FAS Enterprise Data Architecture Github Repository
40_has_ato: Yes
41_system_name: EDA
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
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: Documentation has been developed: Complete documentation detailing model performance across a range of benchmarks, architecture, relevant features and information regarding the appropriate use of the model for predictive tasks has been created.
51_extension_request: No – Agency did not request an extension for this use case.
52_impact_assessment: No
53_real_world_testing: Benchmark evaluation: Testing of the AI model has involved the use of benchmarks (either publicly available or internally created) to estimate performance in real-world settings, but has not been tested in an operational environment. 
54_key_risks: Over or underestimation and therefore mistargeting initiatives around KPIs 
55_independent_eval: Planned or in-progress
56_monitor_postdeploy: No monitoring protocols have been established: Necessary infrastructure needed to perform monitoring of AI systems in production is not available and/or a plan to monitor models has not yet been established.
57_autonomous_impact: No - Some individual decisions or actions require direct human oversight.
59_ai_notice: Online - in the terms or instructions for the service.
61_adverse_impact: No
63_stakeholder_consult: None of the above
65_appeal_process: No – Law, operational limitations, or governmentwide guidance precludes an opportunity for an individual to appeal.
66_no_appeal_reason: Law, operational limitations, or governmentwide guidance precludes an opportunity for an individual to appeal.
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.