GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Microsoft Copilot Integration
3_agency: Election Assistance Commission
3_abr: EAC
4_bureau: OCIO
8_topic_area: Mission-Enabling
10_commercial_ai: Summarizing the key points of a lengthy report using AI.
11_purpose_benefits: Efficiency and improved administrative tasks.
12_outputs: Text-based summaries and AI generated text. 
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
51_extension_request: No – Agency did not request an extension for this use case.
52_impact_assessment: No
53_real_world_testing: Performance evaluation in operational environment: The AI use case has been tested in an operational environment before being fully implemented as a solution, or has been tested in simulated or controlled environment using operational or synthetic data.
54_key_risks: AI is not safety or rights-impacting.
55_independent_eval: AI is not safety or rights-impacting.
56_monitor_postdeploy: AI is not safety or rights-impacting.
57_autonomous_impact: AI is not safety or rights-impacting.
59_ai_notice: AI is not safety or rights-impacting.
61_adverse_impact: No
62_disparity_mitigation: AI is not safety or rights-impacting.
63_stakeholder_consult: None of the above
65_appeal_process: No – it is not operationally practical to offer this.
66_no_appeal_reason: AI is not safety or rights-impacting.
67_opt_out: Other
67_question_type: AI is not safety or rights-impacting, is not required for the completion of any tasks, and is optional for individual employees to use.
2_use_case_name: Clearinghouse Network 
3_agency: Election Assistance Commission
3_abr: EAC
4_bureau: Clearinghouse
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: Summarizing the key points of a lengthy report using AI.
11_purpose_benefits: The Clearinghouse Network is a series of online communities hosted on the Roundtable platform. These communities, including any AI features, are not open to the general public. Members of these communities are state or local election officials. The AI tools in this platform are intended to meet the needs of election officials by providing summaries for longer text or video based content. 
12_outputs: Summarized text and video transcripts.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Language Translation
3_agency: Equal Employment Opportunity Commission
3_abr: EEOC
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: Improving the quality of written communications using AI tools.
11_purpose_benefits: Improve quality, efficiency and/or access to services
12_outputs: Generation. Text to speech. Speech to Text.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Reasonable Accommodation
3_agency: Equal Employment Opportunity Commission
3_abr: EEOC
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: Transcribing and summarizing a recorded meeting or interview using AI.
11_purpose_benefits: Improve quality, efficiency and/or access to services
12_outputs: Generation. Text to speech. Speech to Text.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Virtual Assistants
3_agency: Equal Employment Opportunity Commission
3_abr: EEOC
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: Summarizing the key points of a lengthy report using AI.
11_purpose_benefits: Improve quality, efficiency and/or access to services
12_outputs: Generation. Text to speech. Speech to Text.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Employee Training and Professional Development
3_agency: Equal Employment Opportunity Commission
3_abr: EEOC
8_topic_area: Education & Workforce
10_commercial_ai: Collaborating in real-time using AI-assisted tools in word processors.
11_purpose_benefits: Improve quality, efficiency and/or access to services
12_outputs: Generation. 
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Legal Research & Discovery
3_agency: Equal Employment Opportunity Commission
3_abr: EEOC
8_topic_area: Law & Justice
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: Improve quality, efficiency and/or access to services
12_outputs: Generation. Relevant legal information to support enforcement and/or litigation activity
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Records Management
3_agency: Equal Employment Opportunity Commission
3_abr: EEOC
8_topic_area: Mission-Enabling
10_commercial_ai: Inputting large amounts of data from paper forms into a digital system using AI.
11_purpose_benefits: Establish and maintain integrity of electronic data
12_outputs: Generation. create digital documents or information that is organized, indexed, and accessed according to compliance standards.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: FOIA
3_agency: Equal Employment Opportunity Commission
3_abr: EEOC
8_topic_area: Mission-Enabling
10_commercial_ai: Prioritizing and categorizing incoming emails using AI.
11_purpose_benefits: Improve FOIA process
12_outputs: Generation. categorization/classification of priority or topic for policy or legal public comments
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Cybersecurity
3_agency: Equal Employment Opportunity Commission
3_abr: EEOC
8_topic_area: Mission-Enabling
10_commercial_ai: Identifying unusual patterns in system logs from a single incident report using AI.
11_purpose_benefits: Strengthen Cybersecurity practices
12_outputs: Anomalies. Classification.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Invoice and Contract Data Extraction
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Chief Information Officer Organization
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: The purpose is to extract data from invoices and contracts (PDFs) and use the data for reconciliation.  The benefit is to improve the accuracy of invoice and  contract reconciliation and to reduce the time for Oversight Managers (OMs) to perform this activity.
12_outputs: The system generates an excel output sent via email to OMs that show the results of the reconciliation, including discrepancies and errors.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 3/15/2024
19_date_acq_dev_began: 4/15/2024
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: FDIC does not use PIIDs
24_hisp_support: No
31_data_catalog: Other
31_question_type: FDIC's Enterprise Data Catalog is still being implemented
33_agency_data: Data from FDIC invoices and contracts
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.
40_has_ato: Yes
41_system_name: Commodities Invoice Reconciliation
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Other
43_question_type: Various intake processes are available depending on the nature and scope of the request
45_compute_request: Other
45_question_type: Various resource request processes are available depending on the nature and scope of the request
47_timely_resources: Other
47_question_type: Communication timeliness depends on the process relevant to the request. Many processes are automated and status updates are push instantaneous or are available on demand. Other manual processes involve the requestor at each step for transparency.
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: Server Failure Prediction
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Chief Information Officer Organization
8_topic_area: Mission-Enabling
10_commercial_ai: Identifying unusual patterns in system logs from a single incident report using AI.
11_purpose_benefits: Server outages create work stoppages for FDIC.  This use case will use predictive intelligence to predict when a server is about to fail, and will use natural language processing and query to create a help desk ticket for a server about to fail.  
12_outputs: One output will be the predictive intelligence list of servers likely to fail. A second output will be the system generated help desk ticket. 
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
2_use_case_name: Plain Language Policy Assistant
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Chief Information Officer Organization
8_topic_area: Mission-Enabling
10_commercial_ai: Improving the quality of written communications using AI tools.
11_purpose_benefits: Policy writers at the FDIC need to ensure drafted policies comply with the Plain Language Writing Act. The system would be an AI that provided suggested feedback to the user's draft to better comply with the Plain Language Writing Act (PLWA)
12_outputs: The AI systems outputs would provide feedback on the evaluated the draft for clarity and simplicity, active voice, conciseness, jargon and acronyms.  The AI would also redraft selected sections, with PLWA principles applied.


16_dev_stage: Initiated
17_impact_type: Neither
2_use_case_name: Knowledge Article Generation
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Chief Information Officer Organization
8_topic_area: Mission-Enabling
10_commercial_ai: Improving the quality of written communications using AI tools.
11_purpose_benefits: The problem is that staff spends time writing article to resolve server outages.
Following the remediation of a server outage incident, the Gen AI would write a draft Knowledge Article on how to resolve future incidents of that type. Other benefits:

~Providing a better service experience: The Infrastructure Operations Systems Branch (IOSB) team can leverage self-service; they can easily find answers; the IOSB service experience will improve, allowing IOSB team to self serve 24/7 from the Knowledge Articles base
~Reduce caseload on IOSB; IOSB can solve their problems more efficiently, fewer cases require IOSB support. IOSB freed up to solving more complex customer issues
~Cut down average handle time: As we solve server outage cases and structure into articles, more IOSB team members can use that information to solve their cases faster and reduce average handle time.

12_outputs: The output would be a draft Knowledge Article.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
2_use_case_name: AI Coding Assistant
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Chief Information Officer Organization
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Enhance coding speed and accuracy by offering code suggestions in real time, writing unit tests, debugging code, explaining code, adding comments, and translating code across languages. 
12_outputs: Code suggestions and suggested comments to explain code
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 4/18/2023
2_use_case_name: Content Creation
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Corporate University
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Reducing time to create content, increase volume of content, provide content for students who struggle to take notes and engage in class. 
12_outputs: Generative, Provides interactive resources used for training.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 11/4/2024
2_use_case_name: Immersive Learning
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Corporate University
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Immersive learning offers an engaging approach to training programs by creating realistic environments using GenAI where learners can practice and refine their skills. A controlled environment wherein learners interact with immersive technology to solve pre-determined complex situations and problems. This approach amplifies employee engagement and retention, performance management, learning and development, administrative tasks, workforce planning, employee onboarding, recruiting, interviewing as well as others. 
12_outputs: Prediction based on sequencing provided by developer; completions reports with objectives met.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 2/6/2024
19_date_acq_dev_began: 11/22/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: FDIC does not use PIIDs
24_hisp_support: No
31_data_catalog: Other
31_question_type: FDIC's Enterprise Data Catalog is still being implemented
33_agency_data: No specific FDIC datasets go into this model
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.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Other
43_question_type: Various intake processes are available depending on the nature and scope of the request
45_compute_request: Other
45_question_type: Various resource request processes are available depending on the nature and scope of the request
47_timely_resources: Other
47_question_type: Communication timeliness depends on the process relevant to the request. Many processes are automated and status updates are push instantaneous or are available on demand. Other manual processes involve the requestor at each step for transparency.
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: Course Recommendation 
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Corporate University
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Have capability to recommend courses based on history/need and would consider it in the future. This approach amplifies employee engagement and retention, performance management, learning and development milestones, administrative Task, workforce planning, and employee onboarding. 
12_outputs: Recommendation, suggesting learning paths
Provides industry specific training recommendations based previous training listed in your learning path. 
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 7/22/2024
2_use_case_name: Student Monitoring 
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Corporate University
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: To track course progress, comprehension and enable adaptive learning. Improve engagement, to provide performance management, workforce planning, success of employee on-boarding and encourage learning and development. 
12_outputs: Prediction and adaption analyzing and adjusting to support learning 
Provides completion rates, course milestones. 
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 7/22/2024
2_use_case_name: Plagiarism Detection 
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Corporate University
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Detect and prevent cheating and dishonesty,  Promoting the FDIC's values including Fairness, Accountability, and Integrity.  Reduce the time manually inspecting for plagiarism.  
12_outputs: Report with flagged suspected plagiarism, and sources 
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 7/22/2024
2_use_case_name: Student Outcome Projection 
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Corporate University
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Early intervention in the students academic growth providing the ability to support different learning styles and levels helping guide the right educational path forward personalizing learning

Streamlining learning and development for industry and office needs allowing employees reduction of tasks for onboarding and workforce planning and retention. 
12_outputs: Prediction; Curriculum  mapping based on job specific tasks that results in each user and provide position specific training report. 
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 7/22/2024
2_use_case_name: Facilitating Status 
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Corporate University
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Monitor activities and administer evaluations in supervised and unsupervised environments, ensure compliance, and facilitate accreditation of course content allowing industry standards to be met by Corporate University (CU) courses. E.g. Accounting CEUs (Continuing Education Units).
12_outputs: Report that confirms physical attendance for in-person and virtual sessions.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 7/22/2024
2_use_case_name: AI Assisted Data Collection
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Division of Depositor & Consumer Protection
8_topic_area: Other
8_question_type: Financial Regulation
10_commercial_ai: None of the above.
11_purpose_benefits: The Assistant enhances the efficiency and effectiveness of our fair lending and compliance functions by automating data collection from PDF form documents to support internal supervisory consultations.  Through using the Assistant. data collection takes significantly less time, retains and improves accuracy, and allows the FDIC examiners to focus on examination activities
12_outputs: The Assistant provides extracted data from credit reports and similar type documents in excel readable formats
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 4/27/2023
19_date_acq_dev_began: 4/27/2023
22_dev_method: Developed in-house.
24_hisp_support: No
31_data_catalog: Other
31_question_type: FDIC's Enterprise Data Catalog is still being implemented
33_agency_data: This program uses bank-specific data from examinations containing bank-provided customer information from credit reports and loan applications often through FDICConnect
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.
40_has_ato: Yes
41_system_name: Microsoft Power Apps - AI Builder
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Other
43_question_type: Various intake processes are available depending on the nature and scope of the request
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: AI Assisted Time Management
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Division of Depositor & Consumer Protection
8_topic_area: Other
8_question_type: Financial Regulation
10_commercial_ai: Logging and analyzing time spent on tasks using AI-powered time management tools.
11_purpose_benefits: Increase efficiencies of using Process HQ through FOCUS (Framework for the Oversight of Compliance and CRA Activities User Suite) to understand where Examination process steps slow down
12_outputs: A report of time spent and identification of bottlenecks
16_dev_stage: Initiated
17_impact_type: Neither
2_use_case_name: Compliance Risk Monitoring
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Division of Depositor & Consumer Protection
8_topic_area: Other
8_question_type: Financial Regulation
10_commercial_ai: None of the above.
11_purpose_benefits: Enhance effectiveness of off-site risk monitoring on several regulation areas.
12_outputs: A report from Division of Consumer Protection (DCP) Analytics that identifies whether a financial institution has high / low relative risk of a compliance violation.  Used by examiners to scope exams.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 8/14/2023
19_date_acq_dev_began: 8/14/2023
22_dev_method: Developed in-house.
24_hisp_support: No
31_data_catalog: Other
31_question_type: FDIC's Enterprise Data Catalog is still being implemented
33_agency_data: Data comes from three major FDIC data sources, i.e. Research Information System  database (RIS), compliance exam data (Framework for the Oversight of Compliance and CRA Activities User Suite ((FOCUS)) and consumer complaints data ((Enterprise Public Inquiries and Complaints ((EPIC)).

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. 
40_has_ato: Yes
41_system_name: R
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Other
43_question_type: Various intake processes are available depending on the nature and scope of the request
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 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: Analysis of Consumer Complaints
3_agency: Federal Deposit Insurance Corporation
3_abr: FDIC
4_bureau: Division of Depositor & Consumer Protection
8_topic_area: Other
8_question_type: Financial Regulation
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: Identify consumer complaint trends and integrate pre-processing of consumer complaints using AI to enhance adjudication of consumer complaints
12_outputs: Cleaned up and categorized consumer complaints and trend analysis which does into data lake for post-processing analysis.
16_dev_stage: Initiated
17_impact_type: Neither