2_use_case_name: Acquisition Analytics
3_agency: General Services Administration
3_abr: GSA
4_bureau: FAS
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Takes Detailed Data on transactions and classifies each transaction within the Government-wide Category Management Taxonomy
12_outputs: Acquisition Analytics
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 10/1/2018
19_date_acq_dev_began: 4/1/2019
20_date_implemented: 4/1/2019
22_dev_method: Developed with both contracting and in-house resources.
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: Misclassification and subsequent misalignment of transaction throughput through GWAS categories
55_independent_eval: Planned or in-progress
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: 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.