Translation and Transcription for Investigative Data
Description
This use case intends to solve the problem of the time-consuming process of translating and transcribing data for investigative purposes.
Detailed example
The Translation and Transcription Service leverages neural machine translation (NMT) models for text translation and automatic speech recognition (ASR) and deep neural network (DNN) models with normalization for voice-to-text transcription.
AI / analytics pattern
Natural Language Processing: AI that processes, interprets, and shares information in human language.
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
HSI investigators often encounter data from various sources, including legal and administrative processes, enforcement actions, and open-source materials, in languages other than English. To unlock the value of this data, it must be translated into English before further analysis can be conducted. The Translation and Transcription Service leverages neural machine translation (NMT) models for text translation and automatic speech recognition (ASR) and deep neural network (DNN) models with normalization for voice-to-text transcription. This innovative approach enables users to quickly triage large datasets and identify key information relevant to investigations. Any data deemed critical for court proceedings is then submitted to certified human translators for final review, ensuring that government resources are allocated efficiently and only used for necessary translations and transcriptions.
Audit / financial statement impact
This use case provides more efficient data processing for HSI personnel. The AI output may be used to produce investigative insights in the form of data, information leads or connections that HSI personnel can use to inform investigations, but the output itself is data preparation and organization so HSI personnel can produce those leads when combining the AI output with the personnel’s expertise and other relevant investigative data and information. Personnel may use these insights for law enforcement purposes in ongoing investigations with existing targets to assist in activities such as producing risk assessments about individuals or identifying criminal suspects; however, all insights are reviewed and validated by both personnel and supervisors before being included in any official case management system, and do not serve as a principal basis for law enforcement action or decision. Any enforcement decisions related to these insights are outcomes of the full Federal Investigation Process involving verifying any insights as evidence (including validating AI-translated material by a certified interpreter), presentation to a U.S. Attorney’s Office and potentially a District Court judge, decision to prosecute, judicial review, and trial and sentencing.
Controls / human review
ATO: Yes; PIA: https://www.dhs.gov/sites/default/files/2025-06/25_0618_priv_pia-ice-055-raven-appendix-update.pdf
Data needed
AI models within the use case are not trained on agency data. Open-source models (i) Whisper is trained on 680K hours of multilingual and multitask supervised data collected from the web, (ii) No Language Left Behind (NLLB) is trained on a combination of publicly available datasets (additional information available in Section 5 of Meta’s NLLB whitepaper: https://research.facebook.com/file/585831413174038/No-Language-Left-Behind--Scaling-Human-Centered-Machine-Translation.pdf).