GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Stock Market Analysis
3_agency: Board of Governors of the Federal Reserve System
3_abr: FRB
4_bureau: Division of Research and Statistics
8_topic_area: Other
8_question_type: Economic & Financial
10_commercial_ai: None of the above.
11_purpose_benefits: To understand media narratives associated with stock market events
12_outputs: Time series measuring association of different news topics frequencies with market volatility
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 9/1/2022
19_date_acq_dev_began: 9/1/2022
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: Yes
33_agency_data: External- News  Feed
34_data_docs: Documentation is missing or not available: No documentation exists regarding maintenance, composition, quality, or intended use of the training and evaluation data.
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: 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: 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.
2_use_case_name: Earnings Calls Search Tool
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: Searching for information using AI.
11_purpose_benefits: NLP tool that summarizes earnings calls to capture key takeaways
12_outputs: Interface to query various sections of public earnings reports

16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Incident Reporting Pattern Detection
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-based security risks
12_outputs: Problem notifications to administrators
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
2_use_case_name: Consumer Related Information Classification
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: Classification of consumer related information to relevant regulations
12_outputs: Assignment of one or more regulations to a consumer related information
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 3/16/2023
19_date_acq_dev_began: 5/1/2023
20_date_implemented: 10/19/2023
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- Firm Data
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: General Support System
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: 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.
2_use_case_name: Commercial Real Estate Index
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: Identify principal component using various data points to create a market index for commercial real estate
12_outputs: A principal component, which then gets used as the index
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 10/1/2023
19_date_acq_dev_began: 10/1/2023
20_date_implemented: 2/1/2024
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: Yes
28_iqa_compliance: The data science team has a quality control framework that the use case has gone through prior to being posted to the public website.
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: External - Real Estate
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: 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.
2_use_case_name: Variable Optimization
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: Iterate through alternative lag structures to identify the optimal one for forecasting call report metrics
12_outputs: A forecast for select call report metrics
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 1/1/2022
19_date_acq_dev_began: 2/1/2022
20_date_implemented: 9/28/2023
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
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: General Support System
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: 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.
2_use_case_name: Credit Fragment Analysis
3_agency: Board of Governors of the Federal Reserve System
3_abr: FRB
4_bureau: Division of Research and Statistics
8_topic_area: Other
8_question_type: Economic & Financial
10_commercial_ai: None of the above.
11_purpose_benefits: Research on credit fragments
12_outputs: Identification of observations
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 11/1/2022
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
2_use_case_name: Cybersecurity Monitoring Tool
3_agency: Board of Governors of the Federal Reserve System
3_abr: FRB
4_bureau: Division of Supervision and Regulation
8_topic_area: Other
8_question_type: Economic & Financial
10_commercial_ai: Summarizing the key points of a lengthy report using AI.
11_purpose_benefits:  Monitors emerging cyber risks to the financial sector 
12_outputs: A risk ranking algorithm trained on a dictionary of weighted terms that imply potential financial system risk
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Proposals and Public Comments
3_agency: Board of Governors of the Federal Reserve System
3_abr: FRB
4_bureau: Office of the Secretary
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: The Board of Governors of the Federal Reserve System (“Board”) is developing the Proposals and Public Comments (PPC) system to electronically process and manage comments from the public on regulatory rulemakings, information collections, and other proposals (collectively, “proposals”) and to post those comments to the Board’s public website. The first release of PPC is expected in mid-November 2024. The Board’s processing of comments may use artificial intelligence (AI) to provide more efficient processing of public comments (e.g., PII redaction recommendations, spam detection).
12_outputs: The Board’s processing of comments may use artificial intelligence (AI) to provide more efficient processing of public comments (e.g., PII redaction recommendations, sentiment analysis, text matching, entity identification, and text similarity matching) . A human verifies the system recommendations.

16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 9/1/2023
19_date_acq_dev_began: 9/1/2023
20_date_implemented: 11/16/2024
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: No
33_agency_data: External- Public Comments
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: Proposals and Public Comments
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: No
49_existing_reuse: Re-use production level code and/or data products: This use case re-uses production level code as well as data products that have been published or made widely available for data science teams for within the agency.
50_internal_review: No documentation is available: No documentation detailing model performance metrics, model architecture, features and intended use of the models have been created or are currently accessible to other data science teams within the agency.
2_use_case_name: Graphic design improvements
3_agency: Board of Governors of the Federal Reserve System
3_abr: FRB
4_bureau: Division of Board Members
8_topic_area: Other
8_question_type: Economic & Financial
10_commercial_ai: Creating visually appealing presentations using AI-driven design suggestions.
11_purpose_benefits: Enhances readability of presentations
12_outputs: Updated slide layouts and visualizations
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Bank Subsidiary Identification
3_agency: Board of Governors of the Federal Reserve System
3_abr: FRB
4_bureau: Division of Research and Statistics
8_topic_area: Other
8_question_type: Economic & Financial
10_commercial_ai: None of the above.
11_purpose_benefits: Identifying auto loans reported as subsidiaries of the banks
12_outputs: Identification of loans issued by finance company subsidiaries of banks
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 10/1/2024
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
2_use_case_name: Manufacturer Sentiment Analysis
3_agency: Board of Governors of the Federal Reserve System
3_abr: FRB
4_bureau: Division of Research and Statistics
8_topic_area: Other
8_question_type: Economic & Financial
10_commercial_ai: None of the above.
11_purpose_benefits: To quantify survey responses related to forecasts of industrial production
12_outputs: Time series measuring the sentiment of manufacturing via survey respondents
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 7/1/2022
19_date_acq_dev_began: 7/1/2022
20_date_implemented: 7/1/2022
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: External- Manufacturing
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: 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: 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.
2_use_case_name: Supply Chain Estimations
3_agency: Board of Governors of the Federal Reserve System
3_abr: FRB
4_bureau: Division of Research and Statistics
8_topic_area: Other
8_question_type: Economic & Financial
10_commercial_ai: None of the above.
11_purpose_benefits: To estimate supply chain bottlenecks
12_outputs: Time series measuring supply chain bottleneck sentiment data
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 12/1/2022
19_date_acq_dev_began: 12/1/2022
20_date_implemented: 12/1/2022
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: Yes
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Internal- Beige Book
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: 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: 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.
2_use_case_name: Research And Development Analysis
3_agency: Board of Governors of the Federal Reserve System
3_abr: FRB
4_bureau: Division of Research and Statistics
8_topic_area: Other
8_question_type: Economic & Financial
10_commercial_ai: None of the above.
11_purpose_benefits: To quantify AI research and development intensity in industry
12_outputs: Time series measuring R&D intensity within industry
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 8/1/2024
19_date_acq_dev_began: 8/1/2024
20_date_implemented: 8/1/2024
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 and External- Government and Economic Data- 10K filings, FRED Macroeconomic Data
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: 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: 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.
2_use_case_name: Newsfeed Data Transformations
3_agency: Board of Governors of the Federal Reserve System
3_abr: FRB
4_bureau: Division of International Finance
8_topic_area: Mission-Enabling
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: Applying labels for country of origin and topic to articles
12_outputs: Article labels for entities, topics, and sentiment
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Newsfeed Data Processing
3_agency: Board of Governors of the Federal Reserve System
3_abr: FRB
4_bureau: Division of International Finance
8_topic_area: Mission-Enabling
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: To improve the processing of news articles
12_outputs: The dataset contains entity extractions, topic modeling, and sentiment analysis
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
2_use_case_name: Body Worn Cameras Data Management System
3_agency: Board of Governors of the Federal Reserve System
3_abr: FRB
4_bureau: Office of the Inspector General
8_topic_area: Law & Justice
10_commercial_ai: Transcribing and summarizing a recorded meeting or interview using AI.
11_purpose_benefits: Audio recordings may be transcribed to text and/or redacted. Transcripts are
then labeled “unverified” until an OIG designated member reviews, edits and approves the final transcript.

Automatically detects and redacts screens (computer screens, digital
signs), faces and license plates captured. Prior to redacting any evidence, a special agent must first approve the redactions before sharing any evidence.
12_outputs: Text versions of spoken word
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
2_use_case_name: Market Fund Portfolio
3_agency: Board of Governors of the Federal Reserve System
3_abr: FRB
4_bureau: Division of Research and Statistics
8_topic_area: Other
8_question_type: Economic & Financial
10_commercial_ai: None of the above.
11_purpose_benefits: To assist with issuer classification of money market funds
12_outputs: A list of likely issuers of a particular security
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 10/1/2020
19_date_acq_dev_began: 10/1/2020
20_date_implemented: 5/1/2021
22_dev_method: Developed in-house.
24_hisp_support: No
25_hisp_name:  
27_public_info: No
28_iqa_compliance:  
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: External- SEC Filings
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: General Support System
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
43_question_type:  
45_compute_request: No
45_question_type:  
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.
2_use_case_name: Earnings Calls Analysis
3_agency: Board of Governors of the Federal Reserve System
3_abr: FRB
4_bureau: Division of Research and Statistics
8_topic_area: Other
8_question_type: Economic & Financial
10_commercial_ai: Summarizing the key points of a lengthy report using AI.
11_purpose_benefits: Analyzes company transcripts to identify key words, terms, and themes.
12_outputs: Suite of time series indicators showing evolution of word choice and topic classification
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Sentiment Analysis of Earnings Transcripts
3_agency: Board of Governors of the Federal Reserve System
3_abr: FRB
4_bureau: Division of Monetary Affairs
8_topic_area: Other
8_question_type: Economic & Financial
10_commercial_ai: None of the above.
11_purpose_benefits: Additional insight into sentiments related to topics in bank earnings calls
12_outputs: The probability that text inputs are positive, negative, or neutral and classifies them as such
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 5/23/2023
19_date_acq_dev_began: 6/1/2023
20_date_implemented: 10/1/2024
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
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: General Support System
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
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: 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.