GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Supply and Demand Tool
3_agency: Board of Governors of the Federal Reserve System
3_abr: FRB
4_bureau: Division of Supervision and Regulation
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits:  The tool is used to monitor and project resource and supply demand​ based on defined rule sets
12_outputs: Resource monitor based on rule outcomes
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 1/1/2020
2_use_case_name: Outlier Detection
3_agency: Board of Governors of the Federal Reserve System
3_abr: FRB
4_bureau: Division of Supervision and Regulation
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Traditional AI to help identify, synthesize, and deliver information provided by firms.
12_outputs: Identify, Synthesize, and Deliver Information from firms
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 6/1/2021
2_use_case_name: Earnings Call Topic Model
3_agency: Board of Governors of the Federal Reserve System
3_abr: FRB
4_bureau: Division of Supervision and Regulation
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Model used to classify topics from earnings calls
12_outputs: Classify Information from Earnings Calls
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 1/1/2021
2_use_case_name: Bank Performance Monitoring
3_agency: Board of Governors of the Federal Reserve System
3_abr: FRB
4_bureau: Division of Supervision and Regulation
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Model used to support off site risk analysis
12_outputs: Econometric model that supplements offsite risk analysis based on
historical ratings
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 3/1/2022
2_use_case_name: Network management platform
3_agency: Board of Governors of the Federal Reserve System
3_abr: FRB
4_bureau: Division of Information Technology
8_topic_area: Mission-Enabling
10_commercial_ai: Identifying unusual patterns in system logs from a single incident report using AI.
11_purpose_benefits: Automatic detection of network configuration issues
12_outputs: Real-time notifications to administrators
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
2_use_case_name: Bank Exam Quality Control
3_agency: Board of Governors of the Federal Reserve System
3_abr: FRB
4_bureau: Division of Supervision and Regulation
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: A traditional machine learning NLP model of bank ratings that utilizes information in news articles among various inputs
12_outputs: Model outcomes are used as a component of quality control
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 1/1/2022
19_date_acq_dev_began: 1/1/2022
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: External- News  Feed
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: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: General Support System
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: No
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: Recommendation Engine
3_agency: Board of Governors of the Federal Reserve System
3_abr: FRB
4_bureau: Division of Supervision and Regulation
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: Curating news articles and updates based on user preferences using AI.
11_purpose_benefits: Connect users with content by providing suggestions based on the user's own view history and the view history of other users who viewed similar content
12_outputs: 1) Recommends similar documents to users based on the document that they are currently viewing, and 2) recommends documents to users based on their document view history
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Risk Rating Model- Community Banks
3_agency: Board of Governors of the Federal Reserve System
3_abr: FRB
4_bureau: Division of Supervision and Regulation
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: To improve upon the risk classification framework used to tier community banks according to risk, and to tailor examination intensity according to risk
12_outputs: Statistical methods are used in the variable selection process for a risk model
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 5/1/2020
19_date_acq_dev_began: 5/2/2020
20_date_implemented: 10/30/2023
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Internal- FFIEC Call Report, Uniform Bank Performance Report
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.
40_has_ato: Yes
41_system_name: General Support System
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Other
45_question_type: This use case does not require intensive compute resources, but yes there is a process for requesting access to the compute environment where models are trained/estimated.
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: Risk Rating Model
3_agency: Board of Governors of the Federal Reserve System
3_abr: FRB
4_bureau: Division of Supervision and Regulation
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: Identify a set of factors that may be helpful in predicting  the likelihood that a bank will experience an adverse outcome in the future, and to tier banks into High-, Moderate- and Low-risk tiers for review

12_outputs: Statistical methods are used in the variable selection process for a risk model
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 10/1/2019
19_date_acq_dev_began: 10/2/2019
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Internal- FFIEC Call Report, Uniform Bank Performance Report
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.
40_has_ato: Yes
41_system_name: General Support System
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Other
45_question_type: This use case does not require intensive compute resources, but yes there is a process for requesting access to the compute environment where models are trained/estimated.
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: Automatic PSC Classification
3_agency: Federal Trade Commission
3_abr: FTC
4_bureau: BCP
8_topic_area: Other
8_question_type: Consumer Response and Operations
10_commercial_ai: None of the above.
11_purpose_benefits: FTC receives uncategorized complaints from multiple channels and contents of comments varies widely by source.


12_outputs: Product and Service code
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 7/10/1905
19_date_acq_dev_began: 7/10/1905
20_date_implemented: 7/11/1905
22_dev_method: Developed with contracting resources.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: Consumer Sentinal Network database
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: Sentinal Network System
43_infra_provisioned: Yes
45_compute_request: Yes
2_use_case_name: Group Duplicate Complaints
3_agency: Federal Trade Commission
3_abr: FTC
4_bureau: BCP
8_topic_area: Other
8_question_type: Consumer Response and Operations
10_commercial_ai: None of the above.
11_purpose_benefits: Consumers submit multiple complaints for the same issue using various channels, which impacts the ability of Law Enforcement to quickly search and identify leads.


12_outputs: Links to duplicate complaints
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 7/10/1905
19_date_acq_dev_began: 7/10/1905
20_date_implemented: 7/11/1905
22_dev_method: Developed with contracting resources.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: Consumer Sentinal Network database
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: Sentinal Network System
43_infra_provisioned: Yes
45_compute_request: Yes
2_use_case_name: Chatbot
3_agency: Federal Trade Commission
3_abr: FTC
4_bureau: BCP
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: Delivers unattended chat 24/7 for Identity Tehft.gov and ReportFraud.gov after business hours. Assisted chat is only available during business hours. 
12_outputs: Chats
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 7/10/1905
19_date_acq_dev_began: 7/10/1905
20_date_implemented: 7/11/1905
22_dev_method: Developed with contracting resources.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: Consumer Sentinal Network database
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: Sentinal Network System
43_infra_provisioned: Yes
45_compute_request: Yes
2_use_case_name: Word Cloud
3_agency: Federal Trade Commission
3_abr: FTC
4_bureau: BCP
8_topic_area: Other
8_question_type: Consumer Response and Operations
10_commercial_ai: None of the above.
11_purpose_benefits: Uses natural language processing to visually explore complaints using word cloud and assist Law Enforcement to reduce the time required to analyze search results
12_outputs: word cloud
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 7/10/1905
19_date_acq_dev_began: 7/10/1905
20_date_implemented: 7/11/1905
22_dev_method: Developed with contracting resources.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: Consumer Sentinal Network database
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: Sentinal Network System
43_infra_provisioned: Yes
45_compute_request: Yes
2_use_case_name: Graph Analytics
3_agency: Federal Trade Commission
3_abr: FTC
4_bureau: BCP
8_topic_area: Other
8_question_type: Consumer Response and Operations
10_commercial_ai: None of the above.
11_purpose_benefits: Assist Law Enforcement in identifying connections between complaints
12_outputs: node diagram
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 7/10/1905
19_date_acq_dev_began: 7/10/1905
20_date_implemented: 7/11/1905
22_dev_method: Developed with contracting resources.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: Consumer Sentinal Network database
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: Sentinal Network System
43_infra_provisioned: Yes
45_compute_request: Yes
2_use_case_name: Developer productivity
3_agency: Federal Trade Commission
3_abr: FTC
4_bureau: BCP
8_topic_area: Other
8_question_type: Consumer Response and Operations
10_commercial_ai: None of the above.
11_purpose_benefits: FTC is integrating Generative AI (Gen AI), specifically Azure OpenAI GPT-4 (AOI), into our workflow to boost development efficiency. 
12_outputs: Application code
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 7/14/1905
19_date_acq_dev_began: 7/15/1905
20_date_implemented: 7/16/1905
22_dev_method: Developed with contracting resources.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: Yes
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: Sentinal Network System
43_infra_provisioned: Yes
45_compute_request: Yes
2_use_case_name: Freedom of Information Act (FOIA) Discovery AI Pilot
3_agency: National Archives and Records Administration
3_abr: NARA
8_topic_area: Mission-Enabling
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: NARA is piloting the use of AI to improve its responses to Freedom of Information Act requests. This includes AI tools for finding relevant records and automatically redacting sensitive information, which will make the process faster, more accurate, and compliant with privacy laws.
12_outputs: This AI use case will output relevant records matching FOIA requests and redacted versions of those records with sensitive information removed.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 7/16/1905
19_date_acq_dev_began: 7/16/1905
20_date_implemented: TBD
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: Yes
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: NARA will train this AI use case based on previous FOIA requests that were released to public 
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: No
41_system_name: TBD
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: 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: Planned or in-progress.
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. 
55_independent_eval: Yes – by another appropriate agency office that was not directly involved in the system’s development
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: Direct user testing
2_use_case_name: Auto-fill of Descriptive Metadata for Archival Descriptions 
3_agency: National Archives and Records Administration
3_abr: NARA
8_topic_area: Mission-Enabling
10_commercial_ai: Digitizing text from scanned documents or smart forms for archival purposes using AI.
11_purpose_benefits: NARA is using AI to automatically create descriptions (metadata) for its digital archives. This will save archivists time, make records easier to find, and help the public better understand the information in the National Archives Catalog.
12_outputs: This AI use case will create more complete and informative descriptions for records, making them easier to find in the National Archives Catalog.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 7/16/1905
19_date_acq_dev_began: 7/16/1905
20_date_implemented: TBD
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: Yes
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: The data is publicly available at 
catalog.archives.gov
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: No
41_system_name: Description and Authority Service (DAS)
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: 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: Planned or in-progress.
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. 
55_independent_eval: Yes – by another appropriate agency office that was not directly involved in the system’s development
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: Direct user testing
2_use_case_name: AI based Semantic Search for National Archives Catalog
3_agency: National Archives and Records Administration
3_abr: NARA
8_topic_area: Mission-Enabling
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: NARA is using AI to create a smarter search engine for its online catalog. This new search understands the meaning behind your search, not just the keywords, and can even find hidden connections between documents. This will make research faster, easier, and more insightful.
12_outputs: This AI search tool will provide better search results and show connections between records, making research easier and more insightful.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 7/16/1905
19_date_acq_dev_began: 7/16/1905
20_date_implemented: TBD
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: Yes
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: Agency is strictly using its data to test the pre-trained model for the purpose of prototype work. The data is publicly available at catalog.archives.gov.
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: No
41_system_name: ArchisAI
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: 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: 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. 
55_independent_eval: Yes – by another appropriate agency office that was not directly involved in the system’s development
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: Direct user testing
2_use_case_name: AI Pilot for National Declassification Center (NDC) at NARA
3_agency: National Archives and Records Administration
3_abr: NARA
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: NARA plans to use AI to automate the declassification of documents, making the process faster, more accurate, and transparent. This will improve efficiency, ensure consistency in applying declassification rules, and ultimately make more information available to the public.
12_outputs: This AI use case will output declassified documents with sensitive information redacted, ready for public release.
16_dev_stage: Planned
17_impact_type: Neither
31_data_catalog: Yes
41_system_name: TBD