The Hearing Recording and Transcriptions (HeaRT) system is an AI-enabled software platform deployed by the Social Security Administration (SSA) to record and produce automated transcripts of disability hearings conducted by Administrative Law Judges (ALJs). HeaRT replaces an older hardware-based recording system that had been installed in SSA hearing offices across the United States, shifting the function to a software-based workflow that no longer depends on dedicated physical recording equipment.
The retained source base supports HeaRT as an automated transcription system using speech-recognition technology to convert spoken testimony from disability hearings into written text. SSA's official materials describe the system as using generative AI for automated transcripts, while secondary commentary discusses Automatic Speech Recognition (ASR) and possible additional AI-assisted processing. The public sources do not disclose the exact model architecture, vendor, or whether the deployed system includes any large-language-model layer beyond transcription.
The system supports all hearing formats currently used by SSA, including in-person hearings, telephone hearings, and video hearings, without requiring dedicated recording hardware for each modality. SSA completed the nationwide rollout of HeaRT by 17 March 2025, making it operational across all hearing offices in the country. That matters because the disability hearing process is one of the most procedurally sensitive parts of SSA administration: the hearing record captures claimant testimony, representative argument, and expert input that may later be reviewed on appeal. Reliable capture of the spoken record is therefore central to administrative fairness even if the recording tool itself does not determine eligibility.
HeaRT is aimed at a specific operational bottleneck rather than at adjudication. Prior to rollout, SSA relied on ageing recording hardware that could fail, require maintenance, or be mismatched to newer hearing modalities such as phone and video proceedings. A software-based recording and transcription workflow promises more consistent capture across hearing formats, less dependence on local equipment, and faster production of hearing records that staff and adjudicators can use downstream. In administrative terms, it is a classic process-automation deployment: it removes manual transcription burdens and modernises a legacy support function around a high-volume adjudicative process.
SSA projects that the system will serve approximately 500,000 customers annually who go through the disability hearing process and will save about USD 5 million per year by reducing hardware, maintenance, and manual transcription costs. By removing reliance on physical recording equipment and reducing transcription burdens, HeaRT is intended to reduce hearing disruptions and improve administrative efficiency. The official framing is therefore about continuity of operations and cost reduction as much as about AI modernisation.
The deployment sits within SSA's broader push to use AI-enabled tools in disability administration, but HeaRT itself should be understood narrowly as a recording and transcription tool rather than a benefits-decision system. It does not decide whether a claimant qualifies for SSDI or SSI, nor does it rank claims by merit. Instead, it produces a textual artefact that becomes part of the hearing record. That distinction is important for coding decision criticality: transcript quality can influence later proceedings, but the system is still one step removed from the substantive legal decision.
The deployment of HeaRT sits within the context of SSA's significant operational pressures. As of 2025, SSA faced a backlog of approximately 1.4 million disability claims, with an average processing time of eight months for initial decisions. The agency has pursued multiple AI-enabled tools to address these pressures, including the Quick Disability Determinations (QDD) predictive model and the IMAGEN document processing system, with HeaRT representing the transcription component of this broader modernisation effort.
Acting Commissioner Lee Dudek stated at the rollout completion that the system would allow SSA to "better serve the public by making the hearings process more efficient and less disruptive." The shift from dedicated recording hardware to a software-based platform also reduces the physical infrastructure footprint of SSA hearing offices, eliminating the need for specialised equipment installation and maintenance at each location.
Experts have raised concerns about the accuracy and reliability of automated transcripts in legal proceedings. Public commentary highlights risks tied to accents, environmental noise, legal terminology, fast or unclear speech, overlapping speakers, remote-audio quality, and other sources of transcription error. Those risks are especially relevant in disability hearings, where medical terminology, unfamiliar proper names, or claimant communication difficulties may increase the likelihood of transcription mistakes. Errors in the transcript could misstate testimony or complicate later review if they are not identified and corrected.
Human review therefore remains an important safeguard for the hearing record. Daniel Ho of the National AI Advisory Committee has cautioned that automated transcription systems "can still hallucinate" and that human oversight of the resulting transcripts is essential to protect due-process rights. Legal practitioners have noted that their firms already review hearing recordings as standard practice, providing an existing layer of quality assurance that becomes more important with the introduction of AI-generated transcripts. The accuracy of the automated transcripts is particularly consequential because the hearing record serves as the evidentiary basis for ALJ decisions on disability benefits, and errors in transcription could affect the outcome of appeals or judicial review. Representatives, hearing staff, and adjudicators can compare transcripts with the underlying audio and challenge or contextualise apparent errors. The retained evidence does not describe a fully automated closed loop in which transcript outputs are accepted without scrutiny. Instead, the practical safeguard is continued human use of the transcript as a draft or support record within a larger legal-administrative process.
From a governance perspective, public information remains limited. SSA has not disclosed the vendor, the exact ASR stack, the compute environment, or detailed audit metrics such as word error rates, correction volumes, or subgroup accuracy. That means the case is confirmed at the level of deployment and intended outcomes, but not at the level of deep technical transparency. Even so, the evidence is sufficient to support a production-ready case record describing HeaRT as a nationwide AI-enabled transcription system that modernises a major administrative function in the U.S. disability hearing process.