GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: AWS Assisted Software Development
3_agency: National Science Foundation
3_abr: NSF
4_bureau: OCIO
8_topic_area: Mission-Enabling
8_question_type: Developer Tools
10_commercial_ai: None of the above.
11_purpose_benefits: Within software development, AI-driven tools, such as AWS CodeWhisperer, Amazon Q and Bedrock, and integration with development workflow, promise to elevate developer productivity and refine code quality. .
12_outputs: The objective of this pilot is to explore potential integration of these AWS services with DIS Developers IDEs, Jenkins automation, Bitbucket, laying the groundwork for Secure AI Assisted Development at NSF
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 7/15/1905
19_date_acq_dev_began: 7/15/1905
24_hisp_support: No
27_public_info: No
28_iqa_compliance: This is a COTS solution and we are using industry best practices and OEM provided solution 
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: No
33_agency_data: Training data will be derived from both AWS pre trained models as well as Agency generated in the case of Bedrock tuning to NSF use cases
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: No
43_infra_provisioned: No
2_use_case_name: Mine Potential Reviewer's Area of Expertise
3_agency: National Science Foundation
3_abr: NSF
4_bureau: TIP-ITE-ENGINES
8_topic_area: Mission-Enabling
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: Airtable is a low code platform that incorporates Open AI.  The Open AI services are purchased by and the data is kept private to Airtable.  TIP has been approved to use Airtable for several usecases and Airtable has been certified as secure by the NSF OCIO Security Team.  
12_outputs:  to use Airtable to trial Airtable's AI's ability to match potential reviewer expertise to the reviewer.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
2_use_case_name: Finding the State
3_agency: National Science Foundation
3_abr: NSF
4_bureau: TIP-ITE-ENGINES
8_topic_area: Mission-Enabling
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: TIP is authorized to use Airtable for project management including reviewer volunteers and recruitment.  Airtable is a no-code platform that includes a built-in database.  We do not store any PII in our database except for &quot;public PII&quot; such as name, position, work email address, areas of professional expertise, etc.  
12_outputs: 1. Airtable AI is prompted to look at the potential reviewers Linked In profile and identify or update the potential reviewers expertise in its database based on recent updates to professional experience in LinkedIn 2 Search existing potential Submitters database for state associated with submitters institution 
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
2_use_case_name: Enhancing TIP Awards: Efficient Tagging for the 10 Key Technology Areas (KTA)
3_agency: National Science Foundation
3_abr: NSF
4_bureau: TIP/OAD
8_topic_area: Mission-Enabling
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: Per the CHIPS and Science Act mandate, TIP needs to know our investment portfolio based on the 10 KTA.
12_outputs: Enhancing TIP Awards: Efficient Tagging for the 10 Key Technology Areas (KTA)
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
2_use_case_name: Data Access Alternatives: Artificial Intelligence Supported Interfaces
3_agency: National Science Foundation
3_abr: NSF
4_bureau: SBE/NCSES
8_topic_area: Mission-Enabling
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: The key objective of this effort is to create and test machine-learning-backed or “artificial intelligence” (AI)-backed user experiences with federal statistical data. This experience shall improve on the current state of user interactions based on obtaining answers to questions via search engines or emailing federal staff or contractors. 
12_outputs: This project seeks to develop and pilot an AI chat bot (or the like) that answers users text queries submitted via an interface. Answers will be obtained from public statistical data of federal statistical agencies in this project.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 7/16/1905
22_dev_method: Developed with both contracting and in-house resources.
24_hisp_support: No
30_saop_review: Yes
31_data_catalog: Other
31_question_type: Currently we are piloting if this is possible. This chat bot will answer questions. The chat bot will return statistical public data.  Users will always be able to contact the agency via email or phone.
37_custom_code: Yes
2_use_case_name: FOIA processing
3_agency: National Science Foundation
3_abr: NSF
4_bureau: OD/OGC
8_topic_area: Mission-Enabling
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: No tool has been selected yet, exploring tools tht may help with sorting requests
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 7/16/1905
2_use_case_name: Alt text Generator for Images in NSF's Media Hub
3_agency: National Science Foundation
3_abr: NSF
4_bureau: OD/OLPA
8_topic_area: Mission-Enabling
10_commercial_ai: Creating visual representations of data sets for reports and presentations using AI.
11_purpose_benefits: Exploring using a ChatGPT integration with NSF’s Digital Asset Management system (DAM), for the images we host there. 
12_outputs:  to generate alt text for images we'll be using in digital communication products (such as nsf.gov) to ensure they’re accessible to individuals using assistive technology.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 7/16/1905
2_use_case_name: Exploring the use of BERTopic Modeling for a portfolio analysis
3_agency: National Science Foundation
3_abr: NSF
4_bureau: OIA/EAC
8_topic_area: Mission-Enabling
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: Exploring whether the application of BERTopic modeling (or a similar NLP method) is applicable to a portfolio analysis. 
12_outputs: xploring whether it is applicable to portfolio analysis
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 7/16/1905
22_dev_method: Developed with both contracting and in-house resources.
24_hisp_support: No
2_use_case_name: Open-source topic identification for the Public Engagement with Science Initiative data analysis.
3_agency: National Science Foundation
3_abr: NSF
4_bureau: OD/OIA
8_topic_area: Mission-Enabling
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: Exploring the use of unsupervised NLP to identify topical themes within responses submitted to the NSF public engagement questionnaire. The AI system will include multiple open-source techniques used together to achieve this goal.
12_outputs: To develop a cluster model to be applied to all responses. Where feasible, the RTI STEM education experts/analysts will manually map the research topics to NSF research programs.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 7/16/1905
2_use_case_name: Hand-written text extraction using Textract for the Public Engagement with Science Initiative
3_agency: National Science Foundation
3_abr: NSF
4_bureau: OD/OIA
8_topic_area: Mission-Enabling
10_commercial_ai: Digitizing text from scanned documents or smart forms for archival purposes using AI.
11_purpose_benefits: We propose using a Commercial Off the Shelf AWS product, Textract, as a character recognition tool Handwritten text will be an open format question for science and engineering and some optional check boxes for demographic and other data questions.
12_outputs:  to convert handwritten text into machine-encoded text for a postcard.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Ask NSF Initial Pilot
3_agency: National Science Foundation
3_abr: NSF
4_bureau: OCIO/DDAI
8_topic_area: Mission-Enabling
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: Ask NSF initial pilot is intended to test the hypothesis that a chatbot can be rapidly created using an AI Platform.
12_outputs: Answer general questions from existing documents
16_dev_stage: Initiated
17_impact_type: Neither
2_use_case_name: Copilot for Microsoft 365
3_agency: Office of Personnel Management
3_abr: OPM
4_bureau: OCIO/CTO
8_topic_area: Mission-Enabling
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: Copilot for Microsoft 365 is an AI-powered addition to the Microsoft 365 suite that helps users be more productive and creative by working with Microsoft 365 apps and services. It uses large language models (LLMs) and integrates with a user's data and Microsoft Graph. Here are some of the ways that Copilot can assist OPM users in Microsoft 365:
•	Summarize 
•	Draft 
•	Analyze data 
•	Create, edit, and share
•	Automate tasks 
•	Suggest ideas 
•	Personalize
12_outputs: The expected output for Copilot for Microsoft 365 are:
1)	Summarizing emails, meetings, calls, and chats. 
2)	Drafting emails and provide coaching on drafts. 
3)	Analyzing data and identifying insights in Excel. 
 The AI would not make decisions for the users. Instead, it will act as a productivity tool to summarize content and create first drafts for the user to edit. It is the OPM user’s responsibility to validate all content and edit as necessary.
16_dev_stage: Acquisition and/or Development
17_impact_type: Rights-Impacting
2_use_case_name: Insight
3_agency: Social Security Administration
3_abr: SSA
4_bureau: Office of Analytics, Review, and Oversight
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: This AI use case is decision support software used by hearings and appeals-level Disability Program adjudicators to help maximize the quality, speed, and consistency of their decision making.  The use case analyzes the free text of disability decisions and other case data to offer adjudicators real-time alerts on potential quality issues and case-specific reference information within a web application.  The use case increases decision quality and reduces human error.
12_outputs: Insight provides 43 Quality flags, when raised, indicating various inconsistencies, omissions, or Policy non-compliant conditions that exist in the disability decision. 
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 7/24/2015
19_date_acq_dev_began: 10/1/2016
20_date_implemented: 10/1/2017
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: ITSSC
24_hisp_support: Yes
25_hisp_name: Social Security Administration – SSA 
26_public_service: Obtaining audit disability benefits
27_public_info: No
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: Manually curated data sets based on random sampling methodology of hearing and appeals case processing management information
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. 
35_demo_features: Age, Income, Employment Status
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: Insight
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 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: Intelligent Medical Language Analysis Generation (IMAGEN)
3_agency: Social Security Administration
3_abr: SSA
4_bureau: Office of the Chief Information Officer
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: This AI use case helps employees visualize, search, and more easily identify relevant clinical content in medical records.  The use case improves efficiency and accuracy of disability determinations/decisions.
12_outputs: Document annotations on medical records to aid in navigation such as identifying patient encounters, tests, dates, and other relevant information. Possible impairment and SSA Medical Listing Codes are also identified. 
16_dev_stage: Operation and Maintenance
17_impact_type: Rights-Impacting

18_date_initiated: 1/1/2019
19_date_acq_dev_began: 1/1/2019
20_date_implemented: 8/20/2021
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: Agency-wide IT Support Services Contract.  (ITSSC)
24_hisp_support: Yes
25_hisp_name: Social Security Administration – SSA 
26_public_service: Obtaining audit disability benefits
27_public_info: No
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: Historically adjudicated disability claims and outcomes. Manually annotated documents
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.
35_demo_features: Other
35_question_type: Models do not use structured information regarding demographic variables. However, because medical records are the input for the model, demographic variables may be included as unstructured 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: IMAGEN
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: Yes – Agency requested an extension for this use case.
2_use_case_name: Duplicate Identification Process
3_agency: Social Security Administration
3_abr: SSA
4_bureau: Office of the Chief Information Officer
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: This AI use case identifies, flags, and marks duplicates, reducing the amount of time spent to review cases for hearings.
12_outputs: This AI use case identifies, flags, and marks duplicates, reducing the amount of time spent to review cases for hearings.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 8/20/2018
19_date_acq_dev_began: 10/1/2018
20_date_implemented: 9/1/2019
22_dev_method: Developed with contracting resources.
23_contract_piids: 28321324D00060010
ITSSC - Accenture
24_hisp_support: Yes
25_hisp_name: Social Security Administration – SSA 
26_public_service: Obtaining audit disability benefits
27_public_info: No
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: No
33_agency_data: No Agency data used for training
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: No
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: DIP
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: 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: Handwriting Recognition of Forms; Intelligent Document Processing Platform - Forms Classification and Data Extraction 
3_agency: Social Security Administration
3_abr: SSA
4_bureau: Office of the Chief Information Officer
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: This AI use case classify, identify and extract structured, semi-structured forms containing printed and handwritten fields. The use case increases automation, reduces clerical errors and reduces customer wait times.
12_outputs: The output of extracted data fields on the form is represented in JSON format that can be available to downstream systems using API and through other integration mechanism such as queues.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 3/1/2020
19_date_acq_dev_began: 11/1/2020
20_date_implemented: 1/2/2021
22_dev_method: Developed with contracting resources.
23_contract_piids: 28321324D00060010
ITSSC - Accenture
24_hisp_support: Yes
25_hisp_name: Social Security Administration – SSA 
26_public_service: Obtaining audit disability benefits
27_public_info: No
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: No
33_agency_data: No Agency data used for training
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: Form Data Extraction


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: 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: Modernized Development Worksheet (MDW)
3_agency: Social Security Administration
3_abr: SSA
4_bureau: Office of Analytics, Review, and Oversight
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: This AI use case reviews data during case development so it can be better categorized. The use case reduces processing time and customer wait times.
12_outputs: Labels and categorizes new or updated MDW requests
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 7/11/1905
19_date_acq_dev_began: Current model: 2019-2023
20_date_implemented: 12/1/2023
22_dev_method: Developed in-house.
24_hisp_support: Yes
25_hisp_name: Social Security Administration – SSA 
26_public_service: Obtaining audit disability benefits
27_public_info: No
29_contains_pii: Yes
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Model trained from case development worksheets prepared by SSA employees.
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
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: 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: Anomalous iClaim Predictive Model
3_agency: Social Security Administration
3_abr: SSA
4_bureau: Office of Analytics, Review, and Oversight
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: This AI use case identifies high-risk iClaims. These claims are then sent to Operations for further review before additional action is taken to adjudicate the claims.
12_outputs: Risk scores for recently submitted iClaims.
16_dev_stage: Operation and Maintenance
17_impact_type: Rights-Impacting

18_date_initiated: 10/1/2015
19_date_acq_dev_began: 10/1/2015
20_date_implemented: 3/20/2016
22_dev_method: Developed in-house.
24_hisp_support: Yes
25_hisp_name: Social Security Administration – SSA 
26_public_service: Filing for Social Security retirement benefits
27_public_info: No
29_contains_pii: Yes
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Disclosing this information may impede the agency's ability to effectively combat fraud.
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. 
35_demo_features: Disclosing this information may impede the agency's ability to effectively combat fraud.
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: Anti-Fraud Infrastructure
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: Yes – Agency requested an extension for this use case.