FERC · FERC-0001 · record R2057

Summarization & Policy Analysis for Regulatory Comments (originally Leverage AI in the Rulemaking Process Use Case)

Federal Energy Regulatory Commission · FERC

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Overview

Development stage: DeployedSource: c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: SPARC does not make decisions or take actions that directly affect individual rights, benefits, or services. It supports internal analysis of public comments and does not operate autonomously or outside human oversight. Its outputs are used by analysts to inform summaries and sentiment trends, not to drive determinations or enforcement.
Start date: 2025-10-15 (day precision)Source: 2025-10-15T00:00:00
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Energy and the Environment
Operational functions: Summarization Derived by keyword rules; see methodology

Problem the AI is intended to solve

Addresses duration, efficiencies, and accuracies in initial comment analysis.

Expected benefits

Reduces the time required for comment analysis as part of mission processes.

System outputs

1.Groups public comments by topic and identifies the overall sentiment: positive, negative, or neutral toward each issue. 2.Provides interactive summaries and visualizations that help analysts quickly understand what commenters are saying and how many are engaged on each topic. 3.Enables analysts to ask questions about the comments and receive AI-generated answers, making it easier to explore large volumes of feedback efficiently.

Technology

AI classification: Natural language processingSource: Natural Language Processing: AI that processes, interprets, and shares information in human language.
System name(s): Microsoft Azure Commercial
Custom-developed code: Yes
Public source code: Not reported

Sourcing

How it was built: Contract and in-houseSource: c) Developed with both contracting and in-house resources
Vendor (as reported): Zvolvant (Small Business)
Vendors (standardized): None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII: No
Privacy Impact Assessment: Not reported
Authorization to Operate: Yes
Demographic features: k) None of the above
Training and evaluation data: This uses publicly submitted comments to train and fine-tune its models, and evaluates performance based on how accurately the system summarizes topics, detects sentiment, and responds to analyst queries and acceptance.
Federal Data Catalog: Not reported

Governance

8 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Pre-deployment testingReported in placeSource: a) Yes

AI impact assessmentReported in placeSource: a) Yes

Independent reviewReported in placeSource: c) Yes – by the CAIO

Ongoing monitoringReported in placeSource: a) Yes, sufficient monitoring protocols have been established

Operator trainingReported in placeSource: a) Yes, sufficient and periodic training has been established

Fail-safeReported in placeSource: a) Yes

Appeal processNot applicableSource: b) Not applicable

User and public consultationReported in placeSource: a) Direct usability testing

Potential impacts and how they were identified

Impacts include improved efficiency in comment analysis, reduced manual workload for analysts, and enhanced transparency.