Use AI to populate reviewer "areas of expertise" and state of potential Regional Engines submitters (members of organizations)

Overview

Development stage

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

High-impact designation

Not reported

Impact justification

Not reported

Start date

2024 (year precision)

Withheld from public reporting

Not reported

Mission

Topic area

Not reported

Operational functions

HR and workforce Derived by keyword rules; see methodology

Problem the AI is intended to solve

TIP is authorized to use Airtable for project management, including reviewer volunteers and recruitment. Airtable is a no-code platform that includes a built-in database. This system does not store any PII in the database except for "public PII" such as name, position, work email address, areas of professional expertise, etc. We have two specific use-cases for which we would like to employ Airtable AI: 1. People who volunteer sometimes do not list all areas of their expertise, or expertise becomes stale. We have been able to do a proof of concept where Airtable AI is prompted to look at the potential reviewer's Linked In profile and identify or update the potential reviewer's expertise in its database based on recent updates to professional experience in LinkedIn. We would like permission to have AI complete this work. Typically, an admin person or Program Director will do this work and it is time-consuming and burdensome. AI can do it much faster and a member of the staff can check it rather than complete it for 400-500 reviewers. 2. We also have a database of potential Engines submitters in Airtable -- people who have attended a webinar, roadshow, or submitted at an earlier time and asked to be contacted when new funding opportunities are available. We track very similar information in the potential submitters data base as we do for reviewers who volunteer to review for the program. The information is used to support outreach to these potential submitters when new funding opportunities are announced, or a new Engines event is announced. We need to know the state associated with the particular submitters institution to determine which regional Engine they should be mapped to geographically (state in the U.S.). We have found that we can prompt the AI to look at the potential submitter's institution, and the AI can locate the state 100% of the time to identify the region that the submitter would be associated with if they were accepted to the program. Currently, a staff person does this work, and it is burdensome. The AI can do this work much faster, so that a member of the staff can redirect their time to more value-added work and then go back and validate the AI's work. This permits the NSF Regional Innovation Engines program to segment outreach by event and regional engine . The Airtable AI is an Airtable private instance of OpenAI.

Expected benefits

Not reported

System outputs

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Technology

AI classification

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System name(s)

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Custom-developed code

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Public source code

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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

Not reported

Privacy Impact Assessment

Not reported

Authorization to Operate

Not reported

Demographic features

Not reported

Training and evaluation data

Not reported

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