GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Automated Target Recognition (ATR) Developments for Standard Screening
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: TSA
8_topic_area: Transportation
10_commercial_ai: None of the above.
11_purpose_benefits: The purpose of this use case is to improve upon Automated Target Recognition (ATR) algorithms used to reduce privacy concerns because a TSO is no longer required to view Advanced Imaging Technology (AIT) images. The expected benefits are to increase detection, reduce false alarms, and improve efficiency and passenger experience.
12_outputs: The system reproduces the threat location, which is viewed as a bounding box on a representative human figure, for TSO resolution.
16_dev_stage: Acquisition and/or Development
17_impact_type: Both
18_date_initiated: 6/22/2017
19_date_acq_dev_began: 6/22/2017
22_dev_method: Developed with contracting resources.
23_contract_piids: 70T04023C7573N001
24_hisp_support: Yes
25_hisp_name: Transportation Security Administration – DHS 
26_public_service: Completing TSA passenger screening
31_data_catalog: No
33_agency_data: TSA owns AIT image scans which are used for algorithm development. The scans contain threat images and are potentially sensitive.
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: No
42_dev_tools_wait: More than 12 months
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: Other
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: 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: Accessible Property Screening (APS) Checkpoint CT Prohibited Items (PI) Detection
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: TSA
8_topic_area: Transportation
10_commercial_ai: None of the above.
11_purpose_benefits: This AI helps airplane luggage checks by the TSA officers scanning bags to have a continuous always watching partner to alert them to anything suspicious. 

Currently, a Transportation Security Officer (TSO) who is assigned to every X-ray equipment at an airport checkpoint visually inspects each image. This officer resolves the system generated explosive alarms as well as visually inspecting the image for the presence of non-explosive prohibited items such as guns and sharp objects (see TSA Travel site).
TSA is working on developing new Artificial Intelligence/Machine Learning (AI/ML) algorithms to automate the search for the non-explosive prohibited items (e.g. guns, knives, etc.).  Once a threat is found, the algorithm displays bounding boxes around the suspect item for the operator to then investigate and adjudicate.  
These AI solutions benefit the public by providing a consistent and uninterrupted level of threat detection as an added layer of security. The ML algorithms allow the TSA officers to be more flexible and to better prioritize their attention on important items to improve security.
12_outputs: AI system output is a set of 3-dimentional bounding boxes that is displayed on the X-ray image.  The bounding boxes are placed on top of objects or areas where the algorithm believes it has found a prohibited item (threat object).
16_dev_stage: Acquisition and/or Development
17_impact_type: Both
18_date_initiated: 10/1/2019
19_date_acq_dev_began: 10/1/2019
22_dev_method: Developed with contracting resources.
23_contract_piids: 70T04024F7573N010    - Smiths; 70T04023C7573N003   - SureScan (IDSS); 70T04023D7672N007   - Analogic
24_hisp_support: Yes
25_hisp_name: Transportation Security Administration – DHS 
26_public_service: Completing TSA passenger screening
31_data_catalog: No
33_agency_data: CT images of government scanned bags with threats, additionally have Stream of Commerce CT images collected at several airports. The images are in DICOS or vendor proprietary image format.
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: CheckPoint Property Screening System (CPSS)
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: Other
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: Walk-Through Metal Detector (WTMD) Alternative Automated Target Recognition (ATR) Developments
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: TSA
8_topic_area: Transportation
10_commercial_ai: None of the above.
11_purpose_benefits: Artificial intelligence (AI)-enhanced Millimeter wave (mmWave) detectors are used as an alternative to Walk-Through Metal Detectors (WTMDs) for passenger screening to detect both metallic and non-metallic threats and prohibited items on passengers at the security checkpoint. These detectors are an improvement over traditional walk-through metal detectors and provide increased security and a better passenger experience.
12_outputs: The AI outputs target coordinates to the operator viewing station which is viewed as a bounding box on a representative human figure.
16_dev_stage: Acquisition and/or Development
17_impact_type: Both
18_date_initiated: 9/30/2021
19_date_acq_dev_began: 9/30/2021
22_dev_method: Developed with contracting resources.
23_contract_piids: 70T04024C7573N002
24_hisp_support: Yes
25_hisp_name: Transportation Security Administration – DHS 
26_public_service: Completing TSA passenger screening
31_data_catalog: No
33_agency_data: TSA owns AIT image scans which are used for algorithm development. The scans contain threat images and are potentially sensitive.
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: More than 12 months
43_infra_provisioned: No
45_compute_request: Other
45_question_type: Not yet provisioned.
47_timely_resources: Other
47_question_type: Not yet provisioned.
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: 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: Conversation Training and Feedback Simulator
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: TSA
8_topic_area: Education & Workforce
10_commercial_ai: None of the above.
11_purpose_benefits: TSA's Training and Development office is using an off-the-shelf, on-demand, employee learning/training platform which contains commercial training on many topics tailored to each user's interests and skill level to support their personalized training plans and goals. One available component of the platform uses a large language model (LLM) as part of an interactive, dialogue-style training functionality to communicate with and advise the learner to develop critical non-security sensitive soft skills such as customer service and communicating on difficult topics.
12_outputs: Text responses to questions or answers to a question
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 6/1/2024
19_date_acq_dev_began: 9/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 70RDAD23FR0000044
24_hisp_support: No
31_data_catalog: No
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.
35_demo_features: Sex/Gender
40_has_ato: Yes
41_system_name: Percipio
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: No
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: Answer Engine
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: TSA
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: TSA aims to enhance its capabilities in managing and analyzing complex data, ultimately contributing to more effective and efficient security operations and optimizing the TSA's operational workflows and support capabilities.
12_outputs: This platform is anticipated to harness the power of AI to provide intelligent, context-aware responses and insights.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 10/1/2024
19_date_acq_dev_began: 10/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 70T03024F7667N086
24_hisp_support: No
31_data_catalog: No
33_agency_data: SOP and Policy documents for conducting TSO duties.
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: 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: Automated Field Data Collection
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: TSA
8_topic_area: Transportation
10_commercial_ai: None of the above.
11_purpose_benefits: The AI will analyze screening environments via CCTV footage and extract passenger processing times of various steps within the screening processes. Enabling AI to extract and visualize this data will enable TSA to make data informed decisions while testing or deploying new screening equipment, identify anomalies, establish real-world rates and standards, and reduce or eliminate TSA’s need to deploy data collection teams, resulting in real-time data collection and significantly reduced computational time of findings.

12_outputs: AI system outputs multiple decisions to include screening location performance, rates and standards of the end-to-end screening system, and passenger wait times.

16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 4/1/2023
19_date_acq_dev_began: 9/1/2024
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: 70T04024CSOP7573N001
24_hisp_support: Yes
25_hisp_name: Transportation Security Administration – DHS 
26_public_service: Completing TSA passenger screening
31_data_catalog: No
33_agency_data: Screening process rates & standards.
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: No
42_dev_tools_wait: 6-12 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts within the agency.
50_internal_review: Documentation has been published: Complete documentation has been published to a repository or data catalog within the agency and is made accessible to other data science teams for review and feedback.
2_use_case_name: Plan of Day Staff Optimization
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: TSA
8_topic_area: Transportation
10_commercial_ai: None of the above.
11_purpose_benefits: Plan of Day will automate TSA screening staff optimization.

12_outputs: Staffing operations models prescribing when screening lanes should be opened/closed, when/where screening staff is required to absorb operational peaks, determining optimal gender and certification ratios, recommending when to schedule overtime/shift adjustments, drafting lane rotation plans, and informing national TSA staffing requirements as prescribed optimization plans deviate as airline schedules shift.

16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 10/1/2017
19_date_acq_dev_began: 10/1/2017
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: DHS, S&T Contract Number: 70RSAT21PI00001
24_hisp_support: Yes
25_hisp_name: Transportation Security Administration – DHS 
26_public_service: Completing TSA passenger screening
31_data_catalog: Yes
33_agency_data: STIP CAT data and PMIS passenger throughput data; PIMS historic throughput of screening equipment; Master Checkpoint List/Location Codes; Salesforce Master Airport List; HR data via eTAS, NFC, and OLC; AIMs data for airport logistic information; OAG for flight information; Google weather and traffic 
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: No
42_dev_tools_wait: More than 12 months
43_infra_provisioned: Other
43_question_type: DHS Science and Technology accepted the project via a formal intake process.
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts within the agency.
50_internal_review: Documentation has been published: Complete documentation has been published to a repository or data catalog within the agency and is made accessible to other data science teams for review and feedback.
2_use_case_name: TSA Contact Center Virtual Assistant
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: TSA
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: The three goals for TSA’s virtual assistant are:  1) Improve Ease / Efficiency – deliver timely, accurate responses to customers within / outside of TCC operational hours; 2) Improve Effectiveness – collect new customer insights to help drive agency improvements; 3) Fiscal Responsibility – leverage automation to meet increasing demand without the need for additional resources.
12_outputs: TSA’s virtual assistant, as a Predictive AI tool, will not be able to create and provide original content to customers.  It will be limited to providing responses to customer inquiries based on the existing content in the TCC’s knowledge library.  However, it will record transactional data related to these customer inquiries (e.g., customer inputs, assistant outputs, topic classifications, etc.).

TSA’s virtual assistant will create transactional data regarding customer inquiries in a consistent manner to our existing email and phone channels.  TSA uses this transactional data to guide customer experience improvement efforts.  This data offers TSA insights into:  what is working well, where the pain points are, where to improve public information, and what services are most requested.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 1/10/2024
19_date_acq_dev_began: 8/1/2024
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: 70T01024F5600N002
24_hisp_support: Yes
25_hisp_name: Transportation Security Administration – DHS 
26_public_service: Getting help from TSA
31_data_catalog: Yes
33_agency_data: TSA.gov FAQs (publicly available), TSA Contact Center (TCC) inquiry data (e.g. why customers contacted us; how we responded to them)(not pubicly available), TCC knowledge library (i.e. approved response language; currently used by TCC agents when responding to customers via email and phone channels)(not publicly available; but derived from TSA.gov FAQs).
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: Yes
41_system_name: Customer Service Center - Salesforce
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: No
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: Machine Learning Analysis Applied to Cyber Threat Hunt Data
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: TSA
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Cyber threat hunts typically involve a vast amount of data. Machine learning models can quickly and efficiently process this data as well as more effectively identify anomalous activity than humans. 
This could improve the efficiency and quality of cyber threat hunts by detecting suspicious behavior more quickly and increasing the amount of data that can be analyzed during a hunt.
12_outputs: Currently it is a list of potential anomalies or outliers within the system, but development is still ongoing.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 1/1/2024
19_date_acq_dev_began: 2/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: GS-35F-525GA
24_hisp_support: No
31_data_catalog: No
33_agency_data: Data collected from TSA-owned assets through cyber threat hunt operations.
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: System name is operationally sensitive.
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: Other
49_existing_reuse: Re-use production level code from a different use-case: This use case re-uses production level code and/or internally developed code libraries for model development from existing AI systems or repositories inside 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: Synthetic Data for Improved Automated Threat Recognition (ATR) in Checkpoint Screening
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: TSA
8_topic_area: Transportation
10_commercial_ai: None of the above.
11_purpose_benefits: To create synthetic data that can be used to improve Automated Threat Recognition (ATR) algorithm development. Synthetic data can be quicker to produce which will improve effectiveness by addressing and adapting to new threats quicker.

Accessible Property Screening (APS) and On-Person Screening (OPS) are working with vendors and evaluating AI-based synthetic data generation techniques to bolster the pool of training data available to develop machine learning algorithms in ATR applications.
12_outputs: Images that mimic the human body and various threats.
16_dev_stage: Initiated
17_impact_type: Both
18_date_initiated: 1/11/2023
2_use_case_name: OTA Automated Passenger Screening Gate System
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: TSA
8_topic_area: Transportation
10_commercial_ai: None of the above.
11_purpose_benefits: The purpose is to manage passenger flow while reducing human interaction by using AI to assess body positioning and automatically initiate the screening process when the passenger is in the optimum position.
12_outputs: The AI system initiates an AIT scan when the passenger is in the optimum position. Depending on the results of the AIT scan, the passenger would be routed either for additional screening or to the re-composure area to claim their accessible property and transit into the sterile area.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 8/1/2024
2_use_case_name: Axon Capture Application
3_agency: Department of Homeland Security
3_abr: DHS
4_bureau: TSA
8_topic_area: Law & Justice
10_commercial_ai: None of the above.
11_purpose_benefits: The Transportation Security Administration (TSA) uses body-worn cameras that incorporate artificial intelligence (AI) technologies as a part of the underlying software which transcribes and translates video footage. The technology provides rapid access to LEO and Investigative data, through transcription.  This will cut investigative processing time significantly, while also increasing accuracy.
12_outputs: The AI will transcript audio/video data and provide a printable artifact. The AI only provides recommendations the final usable data is reviewed and certified by TSA staff. 
16_dev_stage: Initiated
17_impact_type: Rights-Impacting

18_date_initiated: 10/1/2023