DEA Drug Signature Program Models

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

Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.

Start date

1998-01 (month precision)Source: 01/1998

Withheld from public reporting

NoSource: a) No

Mission

Topic area

Law Enforcement

Operational functions

No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

Understanding the manufacturing origin and distribution route of illicit drugs by analyzing the chemical composition of seized drugs is a core DEA task. The purpose of this use case is to use AI/ML techniques to develop, maintain, and improve models that enable designated forensic chemists to identify a notional geographic origin or notional manufacturing route of samples selected for DEA's Drug Signature Programs.

Expected benefits

The solution supports designated forensic chemists at DEA to automate analyses and to more quickly identify trend changes regarding drug sample notional geographic origin or notional manufacturing route.

System outputs

Designated forensic chemists responsible for a particular Signature Program are provided with the model's output - a notional geographic region of origin or a notional manufacturing route of samples -- which these forensic chemists then evaluate along with other available information to better understand drug trends.

Technology

AI classification

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

System name(s)

Redacted for cybersecurity purposes.

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)

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

Yes

Demographic features

k) None of the above

Training and evaluation data

The case owner relied on DOJ AI governance practices to select and prepare data, as well as evaluate performance.

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