2_use_case_name: Mail Automation Services
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: VBA: Veterans Benefits Administration
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: Mail Automation Services (MAS) is a ITTT service for intake of VA Claims materials as well as other Veteran, VSO, and stakeholder submissions.  The platform ingests and reviews 25k to 40k packets of information per day, primarily derived from the Centralized Mail Portal.  Utilizing prescribed machine learning and Natural Language Processing (NLP), MAS utilizes Intelligent Form Recognition (IFR), Optical Character Recognition (OCR), Intelligent Character Recognition (ICR), and NLP to extract an average of 95 fields on over 1500 form layouts within VA’s purview.
12_outputs: VBA End-Product establishment and correct business line orientation for ingested submissions are the most common outcomes and outputs for the MAS platform which runs under Veterans Benefits Administration Automation Platform (VBAAP).
16_dev_stage: Operation and Maintenance
17_impact_type: Rights-Impacting

18_date_initiated: 7/1/2019
19_date_acq_dev_began: 10/1/2019
20_date_implemented: 1/6/2020
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: IDIQ 36C10E19D0018   APCAS 36C10D21N0013   MAS 36C10E20N0028   BTP 36C10D23N0010  
24_hisp_support: Yes
25_hisp_name: Veterans Benefits Administration
27_public_info: No
28_iqa_compliance: Quoted below is a section from our white paper on ethical implementation of AI iaw the executive orders and recent legislation: Purposeful and performance-driven: IBM’s NLP services, when combined with OCR/ICR capabilities, enable our platform to interpret unstructured and semi-structured forms, correspondence, and other data types, including handwriting. To date, we have deployed 30+ custom, proprietary NLP models within the VA automation infrastructure, significantly improving upon out-of-the-box OCR capabilities by further extracting and classifying the data necessary for downstream actions. - Accurate, reliable, and effective: Applying NLP significantly improves our extraction quality across form types and variations and allows us to infuse intelligence into the business process. NLP processing enables us to uncover and reveal insights (e.g., identify critical “flash” conditions; linking Veteran written disabilities to a specific condition to include characterizing it as ‘secondary to’) into more complex processing determinations based on the information within the data. Cognitive AI models such as ML, vision, and speech are used throughout our solution to create functional capabilities that continuously improve over time. Safe, secure, and resilient: IBM’s solution resides upon a FedRAMP-certified Amazon Web Services (AWS) GovCloud environment and operates 24/7 within a current VA Authority to Operate (ATO). - Understandable: A foundation of ethical AI/ML techniques is explainability – that is, the ability to understand and explain how an automation arrived at a certain outcome. IBM’s approach to documenting requirements and our detailed processing audits provides the explainability necessary to provide for proper processing outcomes. - Regularly monitored: Supervision and Human-in-the-Loop (HIL) capabilities support immediate data correction and supervised learning to improve extraction accuracy. - Transparent and accountable: IBM’s current process is to provide detailed documentation to VBA prior to the release of new NLP models. This documentation details the purpose and output of the model, the training data and algorithms used, and the limitations and availability of the models. IBM works with VBA throughout the requirements, development, testing and post-production process to review the approach, models and methods used to meet their business requirements.
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: NLP/ML has primarily focused around contents submitted via the centralized mail portal.  Please see the MOU/iSA within eMass and Vasi as the 46 page document delineates data optimization and boundaries.
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: VBAAP - Vasi 2522 eMASS 1143
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
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 published: Complete documentation has been published to a repository or data catalog within the agency and is made accessible to other data science teams for review and feedback.
51_extension_request: Yes – Agency requested an extension for this use case.