Argonne Resilience AI Assistant ARAIA (nee CALLM)

Overview

Development stage

In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.

High-impact designation

Not high-impactSource: c) Not high-impact

Impact justification

The ARAIA (nee CALLM) project will have substantial impact in meeting current administration priorities for a secure and resilient power grid by ensuring that risks to the grid can be mitigated through planning, capital resource allocation

Start date

Not reported

Withheld from public reporting

NoSource: a) No

Mission

Topic area

Energy and the Environment

Operational functions

No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

meeting current administration priorities for a secure and resilient power grid

Expected benefits

The AI, the Argonne Resilience AI Assistant (ARAIA) (nee CALLM - Climate Action through Large Language Models), ARAIA is on track to meet the needs and outcomes outlined in the initial project scope and work plan, but is now better positioned to meet future needs and administration goals. addresses the problem of communicating complex climate projections and scientific literature to a broad audience, particularly electric sector stakeholders. It simplifies this information to help these stakeholders identify climate resilience solutions. Expected benefits include improved communication of climate science, empowering stakeholders to directly address climate change impacts, and accelerating the scalability to serve a wider range of users. This ultimately leads to more effective resilience planning and potentially cost savings through better informed decision-making. Users can interact with the system to retrieve specific data, such as fire weather indices, and receive actionable recommendations on areas like hazard mitigation planning, infrastructure wildfire risk, and comprehensive wildfire impact. This project represents a significant step forward in integrating cutting-edge AI with our resilience planning efforts, ultimately helping communities and decision-makers mitigate the impacts of natural hazards.

System outputs

The AI output of Argonne Resilience AI Assistant (ARAIA) is information synthesized from complex climate projections and scientific literature, presented in a simplified and accessible format. This output helps users understand potential climate impacts and identify appropriate climate resilience solutions. The information is grounded in vetted data and published research to minimize inaccuracies and hallucinations common in large language models. The output could range from summaries of climate-related risks, to lists of potential adaptation strategies tailored to specific situations, depending on the user's input and the function of the system it's integrated with (like ClimRR).

Technology

AI classification

Generative AISource: Generative AI: AI that generates new or synthetic content (e.g., images, videos, audio, text, code).

System name(s)

Not reported

Custom-developed code

Not reported

Public source code

Not reported

Sourcing

How it was built

Not reported

Vendor (as reported)

Not reported

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

No

Demographic features

Not reported

Training and evaluation data

Argonne Resilience AI Assistant (ARAIA) tool utilizes vetted climate data and published climate resilience literature to train, fine-tune, and evaluate its performance. The specific datasets are not detailed here, but the approach emphasizes the use of established climate science information to ground the model's responses and mitigate inaccuracies.

Federal Data Catalog

Not reported

Governance

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

Potential impacts and how they were identified

Not reported