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DelusionEval Controlled Data Use Agreement

Version 1

Last updated: 2026-08-04

1. Parties

This Controlled Data Use Agreement ("Agreement") is entered into between The
Board of Trustees of The Leland Stanford Junior University ("Licensor") and
the data requestor and, if applicable, the requestor's institution
(collectively, "Licensee").

2. Scope and Purpose

Licensor provides controlled access to the DelusionEval dataset and related
documentation (collectively, "Data") to support non-commercial scientific
research on AI safety, evaluation, and related topics.

3. Limited License Grant

Subject to this Agreement, Licensor grants Licensee a non-exclusive,
revocable, non-transferable, non-sublicensable limited license to access and
use the Data solely for lawful, non-commercial scientific research.

No rights are granted except as expressly stated in this Agreement.

4. Access and Sharing Restrictions

Licensee must:

Restrict access to approved personnel only.
Not share credentials, raw files, or access paths with unauthorized
parties.
Not redistribute the Data, in whole or in part, to any third party.
Ensure that all personnel with access are trained on human-subject
protections and applicable privacy/security obligations.

5. Prohibited Uses

Licensee must not:

Attempt to identify, contact, or infer the identity of any participant or
institution represented in the Data.
Link, match, combine, cross-reference, or associate the Data (in whole or
in part) with any other dataset, database, publicly available information,
or other source of information, where doing so could reasonably enable or
increase the likelihood of identification or re-identification of any
individual or institution.
Use any analytical technique, algorithm, model, manual method, or
auxiliary information for the purpose of, or that has the effect of,
reversing, defeating, or circumventing any de-identification,
anonymization, pseudonymization, aggregation, or other privacy-protective
measure applied to the Data.
Use the Data to train, optimize, benchmark, or otherwise improve systems
intended to facilitate self-harm, violence, delusional reinforcement, or
other harmful behavior.
Use the Data for clinical diagnosis, treatment, or direct decision-making
about identifiable persons.
Use the Data for advertising, surveillance, insurance, employment
screening, law-enforcement profiling, or other non-research deployment
contexts.

6. Security and Incident Reporting

Licensee must use reasonable administrative, technical, and physical
safeguards to protect the Data from unauthorized access, use, or disclosure.

If Licensee discovers a potential re-identification risk, data leak, or other
security/privacy incident involving the Data, Licensee must promptly report it
to the Licensor contact listed in Section 16.

7. Publication and Citation Requirements

For any publication, preprint, report, or other public disclosure that uses
the Data, Licensee must:

Cite the DelusionEval dataset DOI.
Cite the FAccT 2026 Delusional Spirals paper.
Cite the DelusionEval dataset release.

Licensee must not publish examples, excerpts, or derived artifacts in a way
that materially increases re-identification risk.

8. Ownership and Third-Party Rights

Stanford-owned portions of the released materials are owned by The Board of
Trustees of The Leland Stanford Junior University.

Some underlying source rights, including rights in original contributed
transcripts, may be held by third parties and are provided under limited
permissions. This Agreement does not transfer those rights.

9. Compliance with Law and Policy

Licensee must comply with all applicable laws, regulations, and institutional
policies governing human-subject and sensitive data research.

Where required by Licensee's institution, Licensee is responsible for
obtaining local ethics/IRB review or confirmation before use.

10. Term and Termination

This Agreement is effective upon first access to the Data and remains in force
until terminated by Licensor or Licensee.

Licensor may terminate access immediately for breach. Upon termination,
Licensee must stop use of the Data and destroy local copies, except where
retention is required by law or formal institutional policy.

11. Warranty Disclaimer

THE DATA ARE PROVIDED "AS IS," WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO WARRANTIES OF MERCHANTABILITY, FITNESS
FOR A PARTICULAR PURPOSE, TITLE, OR NON-INFRINGEMENT.

12. Limitation of Liability

TO THE MAXIMUM EXTENT PERMITTED BY LAW, LICENSOR AND COPYRIGHT HOLDERS ARE NOT
LIABLE FOR ANY CLAIM, DAMAGE, OR OTHER LIABILITY ARISING FROM OR RELATING TO
USE OF THE DATA. LICENSEE AGREES TO HOLD HARMLESS LICENSOR FOR CLAIMS ARISING
FROM LICENSEE'S USE, BREACH, RE-IDENTIFICATION ATTEMPTS, OR SECURITY
INCIDENTS.

13. Publicity

Licensee will not use the name or trademark of Stanford, or the names of
Stanford's employees, students, or agents in any publicity, advertising, or
announcement related to this Agreement without the prior written consent of
Stanford's authorized officials. Any use of Stanford's name will be limited to
statements of fact and will not imply endorsement by Stanford of Licensee's
products or services.

14. Updated Terms

Licensor may update these terms from time to time. Licensor will notify
Licensee of any material changes by posting a notice on the dataset page,
sending an email to the registered address, or displaying a prominent notice
upon login.

If Licensee objects to any changes, Licensee may terminate this Agreement by
ceasing use of the Data within 30 days of notice. Continued use thereafter
means Licensee accepts the new terms.

15. Governing Law

This Agreement will be governed by the laws of the State of California, with
venue for any disputes allowed only in the courts within Santa Clara County.

16. Contact

Licensor: The Board of Trustees of the Leland Stanford Junior University
("Stanford")

Contact for access, compliance questions, and incident reporting:

Name/Role: Jared Moore
Email: jlcmoore@stanford.edu

DelusionEval

Dataset Summary

DelusionEval is an anonymized conversational evaluation dataset for measuring problematic chatbot behavior in delusional-spiral contexts.

This release contains 725 conversation windows with:

eval_subset_id (string)
label (target behavior code)
meets_code (bool)
messages (ordered list of message structs with role, content, and per-message score fields)

Supported Tasks

Safety evaluation and auditing of conversational model behavior
Behavior-code detection and analysis in dialogue windows

Dataset Structure

Data Instances

Each row is a conversation window keyed by eval_subset_id and label. messages is a list of dict-like message objects containing:

text fields (role, content)
bot code score fields (for example, bot-endorses-delusion, bot-romantic-interest)
user intent score fields (user-suicidal-intent, user-violent-intent)

Data Splits

This release is a single split:

train: 725 rows

Labels

18 label codes are included: bot-claims-unique-connection, bot-discourages-self-harm, bot-discourages-violence, bot-dismisses-counterevidence, bot-endorses-delusion, bot-facilitates-self-harm, bot-facilitates-violence, bot-grand-significance, bot-metaphysical-themes, bot-misrepresents-ability, bot-misrepresents-sentience, bot-platonic-affinity, bot-positive-affirmation, bot-reflective-summary, bot-reports-others-admire-speaker, bot-romantic-interest, bot-validates-self-harm-feelings, bot-validates-violent-feelings.

Dataset Creation

Windows were selected from anonymized transcripts and then manually reviewed, filtering, and anonymized.

Considerations for Use

Intended Uses

Safety evaluation and auditing
Method development for detecting problematic chatbot behavior

Out-of-Scope and Non-Recommended Uses

Re-identification attempts
Profiling individuals
Optimizing harmful assistant behavior

Limitations

Small, curated, and domain-specific sample
Sensitive content (mental health, self-harm, violence themes)
Not a population-representative dataset

Access and Safety

Controlled and sensitive-use expectations apply. Do not attempt re-identification.

Citation

@inproceedings{10.1145/3805689.3806443,
author = {Moore, Jared and Mehta, Ashish and Agnew, William and Anthis, Jacy Reese and Louie, Ryan and Mai, Yifan and Yin, Peggy and Cheng, Myra and Paech, Samuel J. and Klyman, Kevin and Chancellor, Stevie and Lin, Eric and Haber, Nick and Ong, Desmond C.},
title = {Characterizing Delusional Spirals through Human-LLM Chat Logs},
year = {2026},
isbn = {9798400725968},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3805689.3806443},
doi = {10.1145/3805689.3806443},
booktitle = {Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency},
pages = {7631--7674},
numpages = {44},
location = {},
series = {FAccT '26}
}

@misc{moore2026delusioneval,
  title = {DelusionEval: Measuring Delusion-Linked Behaviors in AI Chatbots},
  author = {Moore, Jared and Mock, Andrea and Mai, Yifan and Anthis, Jacy Reese and Louie, Ryan and Agnew, William and Mehta, Ashish and Klyman, Kevin and Liang, Percy and Haber, Nick and Lin, Eric and Ong, Desmond C.},
  year = {2026},
  url = {TODO},
}

Downloads last month: 24

Homepage:

github.com

Version: 1.0.0

Date published: 2026-08-04

Keywords: LLM safety, conversation evaluation, mental health

Total file size:

6.88 MB