2_use_case_name: Expansion of Generative AI (GenAI) Caption Generation for all Collections Videos
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: NIH
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: The artificial intelligence (AI) use case builds on an existing Generative AI (GenAI) workflow in NLM Digital Collections that generates transcripts and captions for all videos stored in the Digital Repository dark archive. The expansion of this workflow will apply to all videos and films targeted for Digital Collections. The AI will not be used for content outside of these collections or for non-video materials, as well as already captioned videos. It will integrate with existing video processing systems to automate the captioning process. The types of data utilized will include video files and associated metadata. The deployment will occur within NLM’s Digital Collections management area, with the primary goals being cost savings by reducing reliance on third-party vendors and standardizing video caption workflows across all collections.

The objective is to expand AI-driven captioning across all videos in the NLM Digital Collections, ensuring standardized and accuracy. 
12_outputs: The inputs consist of video or audio mp4 or mp3 format comes from Collections videos that don't have captions. The AI generated captions will be in text format.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 2/1/2020
19_date_acq_dev_began: 3/1/2020
22_dev_method: Developed with contracting resources.
23_contract_piids: HHSN316201200013W/75N97019F00066
27_public_info: Yes
28_iqa_compliance: This use case is operated in compliance with HHS's agencywide Information Quality Act policy and procedures.
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: The agency-owned data used in this AI use case consists of U-Matic videos in MP4 format, hosted on collections.nlm.nih.gov. Each month, 8 uncaptioned U-Matic videos of various length are selected for processing, where the AI-generated captions are applied. The audio is extracted from the video files and used as input for AWS Transcribe, which is a pre-trained model that generates captions. During the QA process, there is a human reviews process that checks the quality and accuracy of the output captions, and a disclaimer is added to the videos indicating that the captions were AI-generated.
34_data_docs: Documentation is complete
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: Yes
45_compute_request: No
47_timely_resources: Yes
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: 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.