GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Geospatial Mapping of Historical Survey Data
3_agency: Department of State
3_abr: STATE
4_bureau: R/GPA/RA
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: Creating visual representations of data sets for reports and presentations using AI.
11_purpose_benefits: Used PowerBI to create an interactive map. Cleaned and collected historical data consisting of all statistically significant results from all Pollfish surveys/tests. This project is retired.
12_outputs: N/A This was retired.
16_dev_stage: Retired
17_impact_type: Neither
18_date_initiated: 1/1/2022
19_date_acq_dev_began: 4/1/2022
20_date_implemented: 5/1/2022
21_date_retired: 12/31/2022
2_use_case_name: AI Tools to Enhance PD Workflows
3_agency: Department of State
3_abr: STATE
4_bureau: R/GPA/RA
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: Improving the quality of written communications using AI tools.
11_purpose_benefits: Plan to leverage ChatGPT Licenses to streamline existing Public Diplomacy workflows with AI tools following Department policy.
16_dev_stage: Planned
17_impact_type: Neither
2_use_case_name: LEO - Budget Office Inquiries
3_agency: Department of State
3_abr: STATE
4_bureau: WHA/EX
8_topic_area: Mission-Enabling (internal agency support)
11_purpose_benefits: Answer questions frequently asked about budget. 
12_outputs: Answers to common questions.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 1/30/2024
19_date_acq_dev_began: 9/5/2024
20_date_implemented: 5/1/2024
22_dev_method: Developed with both contracting and in-house resources.
47_timely_resources: Yes
2_use_case_name: Collections Chatbot
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: This project implemented the SBSE chatbot, which provides self-service Frequently Asked Questions (FAQ) to taxpayers regarding payments, payment plans, financial relief and offer in compromise topics. Three new topics are currently in development and scheduled to be incorporated into the chatbot on 3/13/2025: Automated Substitute for Return (ASFR), Centralized Lien Operation (CLO), and Withholding Compliance (WHC). 

The Intent Engine (using Natural Language Processing (NLP) and AI algorithms) classifies taxpayers' utterances (questions) into intents (requests).  The answers provided to the taxpayers are not created by Generative AI. The responses of the chatbot and business logic is predetermined by content owners. This AI tool aims to classify/navigate to a correct predetermined response.
12_outputs: The NLP algorithm functions by classifying user inputs into predefined topics using advanced natural language processing techniques. It evaluates the input against a set of trained models and assigns a response based on a confidence score calculated by the algorithm. Only responses associated with these pre-established topics are provided, ensuring accuracy and consistency. The algorithm does not generate new or ad-hoc answers outside the defined topics, making it reliable for controlled environments where precision and adherence to predetermined content are critical.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 4/1/2021
19_date_acq_dev_began: 6/1/2021
20_date_implemented: 12/10/2021
22_dev_method: Developed with contracting resources.
23_contract_piids: Contract # NNG15SD27B
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: Yes
34_data_docs: Documentation is widely available: Documentation is not only complete, but is widely accessible within the agency, and has an owner and a regular update cadence.
37_custom_code: No
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: eGain Suite
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
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: 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.
2_use_case_name: Systran Translation Software
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: Systran (SYSTRAN Pure neural Server) language translation software provides language translation capability. It is a Commercial Off-the-Shelf (COTS) product, and version 9 is deployed. Users only use their documentation that is in either English or Non-English, and Systran will translate the content to target language. No documents are stored in Systran. Users are typically IRS staff requiring translation of Non-English to English for cursory evaluation (Triage) of relevancy to case work.  Some users are translating English to a Non-English language as a beginning for manual translation. The Neural Network based translation engine can be augmented with dictionary and translation memories to bolster domain specific language accuracy, and dictionary created during a MITRE task order was added in c. 2020.
12_outputs: Translated content (in target language picked by user) of the source (input) text submitted by the user.  User then can read and review the translation on either a graphical user interface (GUI) field or export. 
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 9/1/2020
19_date_acq_dev_began: 9/1/2020
20_date_implemented: 9/1/2020
22_dev_method: Developed with contracting resources.
23_contract_piids: 2043FY-22-F-00078 P00002
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: No
33_agency_data: Publication 850 and other official translation pairs received from IRS' Human Translation team were used to add this information as translation memories data source to base translation engine.  All such information is non-sensitive. 
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: No
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: RAS-1
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: Yes
47_timely_resources: Yes
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: 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.
2_use_case_name: Automated Underreporter (AUR) Voicebot
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 AUR Voice Bot will authenticate taxpayers using “Shared Secrets” and answer questions requiring account look ups and/or updates. It will give answers to provide confirmation we received their response, the current status of their case, and grant requests for additional time to respond if they are eligible.
12_outputs: The system produces a predefined response that provides taxpayers with information related to their account status or current case details after authentication. It also helps route calls to appropriate service areas or escalate to live agents when necessary.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 2/9/2023
19_date_acq_dev_began: 9/25/2023
20_date_implemented: 5/22/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 2032H5-19-F-00641
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: Uses historical taxpayer interaction data, such as account inquiries and case resolution logs, to train and evaluate performance. Metadata from AUR case files and voice interaction transcripts.
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: No
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: Nuance Digital Contact Center Platform (DCCP)
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
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: 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.
2_use_case_name: Employee Resource Center (ERC) Chatbot
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: This project implemented the ERC chatbot, which provides self-service FAQs to IRS employees regarding administrative support, benefit topics, credit card services, HRConnect questions, retirement, insurance, and various other topics related to the ERC. The Intent Engine (using NLP and AI algorithms) classifies users' utterances (questions) into intents (requests).  The answers provided to the taxpayers are not created by Generative AI. The responses of the chatbot and business logic is predetermined by content owners. This AI tool aims to classify/navigate to a correct predetermined response.
12_outputs: The NLP algorithm functions by classifying user inputs into predefined topics using advanced natural language processing techniques. It evaluates the input against a set of trained models and assigns a response based on a confidence score calculated by the algorithm. Only responses associated with these pre-established topics are provided, ensuring accuracy and consistency. The algorithm does not generate new or ad-hoc answers outside the defined topics, making it reliable for controlled environments where precision and adherence to predetermined content are critical.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 9/30/2022
19_date_acq_dev_began: 3/1/2023
20_date_implemented: 7/20/2023
22_dev_method: Developed with contracting resources.
23_contract_piids: Contract # NNG15SD27B
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: Project utilized data provided by content Subject Matter Experts to evaluate the accuracy of responses.
34_data_docs: Documentation is widely available: Documentation is not only complete, but is widely accessible within the agency, and has an owner and a regular update cadence.
37_custom_code: No
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: eGain Suite
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
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: 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.
2_use_case_name: Taxpayer Services Chatbot
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: This project implemented the W&I chatbot, which provides self-service Frequently Asked Questions (FAQ) to taxpayers regarding refunds and Advanced Child Tax Credit topics. The Intent Engine (using Natural Language Processing (NLP) and AI algorithms) classifies taxpayers' utterances (questions) into intents (requests). The answers provided to the taxpayers are not created by Generative AI. The responses of the chatbot and business logic is predetermined by content owners. This AI tool aims to classify/navigate to a correct predetermined response.
12_outputs: The NLP algorithm functions by classifying user inputs into predefined topics using advanced natural language processing techniques. It evaluates the input against a set of trained models and assigns a response based on a confidence score calculated by the algorithm. Only responses associated with these pre-established topics are provided, ensuring accuracy and consistency. The algorithm does not generate new or ad-hoc answers outside the defined topics, making it reliable for controlled environments where precision and adherence to predetermined content are critical.


16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 2/1/2022
19_date_acq_dev_began: 6/9/2022
20_date_implemented: 3/14/2023
22_dev_method: Developed with contracting resources.
23_contract_piids: Contract # NNG15SD27B
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: Project utilized data provided by content Subject Matter Experts to evaluate the accuracy of responses.
34_data_docs: Documentation is widely available: Documentation is not only complete, but is widely accessible within the agency, and has an owner and a regular update cadence.
37_custom_code: No
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: eGain Suite
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
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: 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.
2_use_case_name: Winnie Chatbot for Employee IT FAQs
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: This project implemented the Winnie chatbot, which provides self-service Frequently Asked Questions (FAQ) to IRS staff regarding various Information Technology (IT) topics. The Intent Engine (using Natural Language Processing (NLP) and AI algorithms) classifies users' utterances (questions) into intents (requests). The answers provided to the users are not created by Generative AI. The responses of the chatbot and business logic is predetermined by content owners. This AI tool aims to classify/navigate to a correct predetermined response.
12_outputs: The NLP algorithm functions by classifying user inputs into predefined topics using advanced natural language processing techniques. It evaluates the input against a set of trained models and assigns a response based on a confidence score calculated by the algorithm. Only responses associated with these pre-established topics are provided, ensuring accuracy and consistency. The algorithm does not generate new or ad-hoc answers outside the defined topics, making it reliable for controlled environments where precision and adherence to predetermined content are critical.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 12/5/2022
19_date_acq_dev_began: 9/26/2023
20_date_implemented: 9/26/2023
22_dev_method: Developed with contracting resources.
23_contract_piids: Contract # NNG15SD27B
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: No
33_agency_data: Project utilized data provided by content Subject Matter Experts to evaluate the accuracy of responses.
34_data_docs: Documentation is widely available: Documentation is not only complete, but is widely accessible within the agency, and has an owner and a regular update cadence.
37_custom_code: No
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: eGain Suite
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
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: 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.
2_use_case_name: DATA Act Bot for Procurement Data Matching
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: Purpose:  The Digital Accountability and Transparency Act (DATA) Act Bot automates verifying that IRS Federal Procurement Data System (FPDS) reporting matches the information in contract documents (e.g. dollar amounts, dates, location of work). Supervised learning and natural language processing are used to extract unstructured information from contract documents. F1 accuracy scores are used to measure performance of validation models for each specific data element.

Benefits: The AI improves operational efficiency by validating consistency and accuracy of contract metadata.
12_outputs: AI model validates that contract spending information reported to USASpending.gov matches contract documents. The system validates the consistency of contract metadata such as contract number, modification number, dollar amounts, contract work / place of performance location address, and contract dates.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 9/26/2019
19_date_acq_dev_began: 9/26/2019
20_date_implemented: 3/31/2022
22_dev_method: Developed with contracting resources.
23_contract_piids: 2032H822F00222
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: IRS contract documents were used to train the model. Project team members manually annotated or created labeled data. Created dataset of information extracted from contract documents (e.g. dates, dollar amounts, addresses). This dataset is used to improve the accuracy and reliability of contract spending reporting.
34_data_docs: Documentation is widely available: Documentation is not only complete, but is widely accessible within the agency, and has an owner and a regular update cadence.
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: Integrated Financial System / Procurement for the Public Sector
42_dev_tools_wait: More than 12 months
43_infra_provisioned: Other
43_question_type: AI component is a contractor managed service application accessed using bot running on agency infrastructure.
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: 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.
2_use_case_name: Taxpayer Services Form 1040 Voicebot
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: This project is implementing 1040 IVR voice bot for Taxpayer Sevices (TS), formally known as Wage & Investments (W&I), which allows them to check their individual tax information, such as Late Filing Season Individual Income Tax Return Menu, Non-Filing Season Individual Income Tax Return Menu. Taxpayer could be directed to the appropriate Customer Service Representative based on their request. . The Intent Engine (using NLP and AI algorithms) classifies taxpayers' utterances (questions) into intents (requests).  The responses of the voicebot and business logic is predetermined by content owners. This AI tool aims to classify/navigate to a correct predetermined response.
12_outputs: Provide caller with information based on business logic/rules.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 2/21/2023
19_date_acq_dev_began: 11/13/2023
20_date_implemented: 3/5/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 2032H5-19-F-00641
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: Call summary/call detail records
34_data_docs: Documentation is widely available: Documentation is not only complete, but is widely accessible within the agency, and has an owner and a regular update cadence.
37_custom_code: No
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: Nuance Digital Contact Center Platform (DCCP)
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
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: 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.