03 · What You Need to Know
What Research Collaboration Agreements Should Address About AI
Why AI Use May Need Explicit Agreement
Research collaboration agreements ordinarily establish the responsibilities and expectations of participating organizations. Their contents vary according to the institutions, funding arrangements, research activities, and applicable legal requirements.
Generative AI can introduce additional questions because research information may be processed by external services, AI-generated outputs may contribute to intellectual work, and collaborators may use tools subject to different contractual conditions.
For example, a collaboration agreement may permit sharing research data between participating universities but prohibit disclosure to unauthorized third parties.
Uploading the same data to an external AI service may involve a separate processing arrangement that was not contemplated when the agreement was signed.
Similarly, a manuscript may contain AI-assisted analysis or writing even though the agreement allocates responsibility only among human contributors.
Explicit AI provisions can clarify how existing obligations apply to these activities.
The objective is not to create an entirely separate governance system for AI. It is to prevent ambiguity where automated assistance intersects with established research responsibilities.
Does Every Collaboration Need an AI-Specific Contractual Clause?
No universal requirement obliges every research collaboration to include an AI-use clause.
Some projects may involve no generative AI, or only routine assistance that presents limited contractual or confidentiality concerns.
Other projects may process sensitive participant information, commercially confidential datasets, unpublished inventions, or research subject to strict institutional restrictions.
For these collaborations, leaving AI use unaddressed may create substantial uncertainty.
A risk-based approach is therefore more defensible than requiring identical contractual language for every project.
When formal AI provisions may be appropriate
If the collaboration involves confidential or restricted research data
Specify whether AI processing is permitted, which services may be used, and what approvals and safeguards are required.
If AI may contribute to consequential research analysis
Define verification responsibilities, decision authority, and documentation expectations.
If collaborators operate under different institutional policies
Establish an agreed procedure for resolving policy conflicts before using the affected tools.
If the project involves intellectual property or commercialization
Clarify how AI-related service terms, confidential inputs, and generated outputs interact with existing intellectual-property provisions.
Define What Counts as AI Use Under the Agreement
Contractual ambiguity can begin with terminology.
One collaborator may interpret AI use as generating manuscript text. Another may include automated transcription, machine-learning analysis, AI-assisted coding, and document summarization.
A useful agreement should define the activities within its scope rather than relying entirely on the phrase "artificial intelligence."
For example, the agreement might distinguish between generative AI services that process prompts and uploaded materials, established analytical software with embedded AI functions, and locally operated computational models.
The distinctions should reflect actual risks and contractual obligations.
Not every automated statistical procedure needs to be treated as equivalent to sending confidential research information to an external generative AI provider.
Specify Permitted, Restricted, and Prohibited Activities
An agreement can identify AI uses that are permitted without additional approval, uses requiring prior authorization, and activities that are prohibited.
These categories should be based on the project's actual circumstances.
| Activity |
Possible contractual treatment |
Reason for review |
| Editing nonconfidential manuscript language |
Permitted subject to applicable policies |
Accuracy, disclosure, and preservation of meaning |
| Generating code using synthetic data |
Permitted with verification requirements |
Implementation accuracy and software security |
| Uploading identifiable participant records |
Restricted or prohibited without appropriate authorization |
Privacy, consent, confidentiality, and lawful processing |
| Analyzing confidential partner datasets |
Prior approval and approved processing environment |
Contractual restrictions and data governance |
| Producing AI-assisted statistical interpretations |
Permitted with qualified human review |
Research validity and accountability |
| Processing unpublished patentable findings |
Subject to intellectual-property and confidentiality review |
Potential disclosure and contractual consequences |
These are illustrative classifications, not universal legal requirements. A particular activity may be prohibited even when it appears low risk if the applicable agreement, protocol, or institutional policy does not permit it.
Address Confidentiality and Research Data Protection
Confidentiality is among the most consequential issues in AI-assisted collaboration.
Researchers may handle participant interviews, unpublished datasets, proprietary algorithms, peer-review materials, or sensitive institutional records.
Entering such information into an external AI service may involve processing by a provider outside the research collaboration.
The agreement should identify what information may be submitted to AI systems, under which conditions, and who can authorize the processing.
Relevant considerations include:
- Whether the information contains personal, confidential, or otherwise restricted data.
- Whether the proposed processing is compatible with participant consent and approved research protocols.
- Which organization controls the data and who may authorize additional processing.
- Whether the provider retains inputs or outputs and for how long.
- Whether submitted information may be used for model training or other secondary purposes.
- Whether subprocessors or cross-border transfers are involved.
- Which technical and organizational safeguards apply.
These questions should be answered using the actual service terms and applicable legal requirements rather than assumptions about AI tools generally.
For collaborations involving personal data, relevant data-protection obligations may apply independently of whether AI is used.
Clarify the Relationship With Data-Processing Agreements
A research collaboration agreement does not necessarily replace a legally required data-processing agreement or other applicable contractual instrument.
For example, the European Union's General Data Protection Regulation establishes requirements for arrangements involving controllers and processors, including provisions under Article 28.
Where a research collaboration or AI service falls within those requirements, the relevant parties must determine which contractual arrangements are necessary.
Other jurisdictions may impose different obligations.
Researchers should not assume that inserting a general AI-use clause satisfies every privacy or data-protection requirement.
Institutional legal and data-protection personnel should review arrangements involving consequential legal obligations.
Identify Which AI Tools and Processing Environments Are Authorized
Permission to use AI should not automatically be interpreted as permission to use every AI service.
Providers may differ in their retention arrangements, security controls, contractual commitments, and available deployment options.
Some institutions may authorize particular enterprise services or locally operated models while restricting public-facing tools for sensitive research information.
An agreement can establish an approved-tool process rather than naming a fixed list that becomes obsolete when services change.
For example, collaborators might require confirmation that a proposed service satisfies agreed security, privacy, and contractual criteria before it processes restricted information.
Any authorization should specify the relevant service, configuration, data category, and permitted activity.
The fact that a provider offers an enterprise product does not automatically establish that every deployment is suitable for confidential research.
Establish Responsibility for Verifying AI-Assisted Work
AI-generated material may contribute to code, statistical analyses, literature summaries, manuscript text, and other research outputs.
The collaboration agreement should clarify who is responsible for evaluating these contributions.
For example, an analyst who uses AI-generated code should ensure that the implementation has been appropriately tested and reviewed.
A researcher who incorporates AI-generated literature summaries should verify substantive claims against original sources.
These responsibilities may be distributed among collaborators according to expertise and project roles.
However, assigning an AI system a task does not transfer human responsibility for the accuracy and integrity of the resulting research.
The agreement should support collective author accountability when AI is used without implying that every collaborator must personally reproduce every specialized operation.
Address Authorship and Intellectual Contributions
Research collaboration agreements often contain provisions concerning publication rights, authorship expectations, and ownership of intellectual contributions.
AI assistance can complicate discussions when collaborators disagree about the significance of AI-generated text, code, or analytical suggestions.
However, AI-generated material should not automatically be treated as a human scholarly contribution.
Under the International Committee of Medical Journal Editors' authorship recommendations, authorship requires qualifying human contributions and accountability. AI-assisted technologies should not be listed as authors.
Other disciplines and journals may use different authorship criteria, but human responsibility remains a central concern.
Agreements should therefore clarify how collaborators will evaluate actual human contributions rather than assigning authorship credit according to the volume of AI-generated material.
The more specific question of whether AI-assisted contributions should affect authorship decisions requires distinguishing the use of a tool from the intellectual work performed by the researcher.
Intellectual Property Requires More Than a General AI Ownership Statement
Ownership and use rights concerning AI-assisted outputs may depend on applicable intellectual-property law, provider terms, existing institutional agreements, and the nature of the human contribution.
A collaboration agreement should not assume that all AI-generated material is automatically copyright-protected or owned exclusively by the person who entered the prompt.
In the United States, for example, the Copyright Office has explained that copyright protection depends on human authorship, with AI-generated material requiring case-specific analysis of the human contribution.
Rules and interpretations may differ across jurisdictions.
Research partners should address relevant rights and restrictions through qualified legal review, particularly when projects involve commercialization, software licensing, patentable discoveries, or proprietary information.
AI-use provisions should complement existing intellectual-property arrangements rather than contradict them.
Specify Disclosure and Documentation Expectations
Collaborators should agree on which AI-assisted activities must be documented and communicated within the team.
The level of detail should reflect the significance of the activity.
Routine language editing may require different documentation from AI-assisted data transformation, model selection, or interpretation.
For consequential uses, a record may identify the tool, its role, the relevant inputs or data categories, the verification performed, and the responsible human reviewer.
Publication disclosures should also comply with the requirements of the target journal or publisher.
Internal documentation and public disclosure serve different purposes. An agreement may require internal records even when a particular journal does not require every use to be described in the manuscript.
Include Procedures for Policy Conflicts
Collaborating institutions may have different AI policies.
One institution may authorize a particular tool while another restricts it for the relevant data or research activity.
The agreement should establish how such conflicts will be identified and resolved.
Possible approaches include using a mutually approved service, separating tasks according to permitted processing environments, or seeking formal authorization before proceeding.
However, collaborators should not assume that the least restrictive institution's policy governs the entire project.
The issue of conflicting institutional AI policies is particularly important when contractual, ethical, and data-protection obligations overlap.
Address Incidents, Unauthorized Use, and Corrective Action
AI-related incidents may include unauthorized disclosure of confidential data, use of an unapproved service, incorporation of unverified generated material, or discovery of consequential errors in AI-assisted analysis.
An agreement can identify how such incidents should be reported, who must be informed, and how corrective action will be coordinated.
Where personal data breaches or other regulated events occur, applicable legal notification requirements may govern the response.
Contractual procedures should not replace those obligations.
The agreement may also establish how collaborators will investigate disputed AI use, preserve relevant records, and determine whether affected research outputs require correction.
These provisions should support proportionate responses rather than automatically treating every AI-related mistake as research misconduct.
Review AI Provisions When the Project Changes
Research collaborations often continue for several years. During that period, AI services, institutional policies, research activities, and data-processing arrangements may change.
A contractual provision that names one approved tool may become outdated.
For this reason, agreements can establish a procedure for approving changes without requiring the entire collaboration agreement to be renegotiated for every minor technical adjustment.
Material changes involving new data categories, external processing arrangements, or consequential research decisions may warrant formal amendment or additional approval.
Such flexibility is particularly important for international research collaborations, where legal and institutional obligations may differ.
06 · What This Means for You
How to Decide What Your Collaboration Agreement Should Include
Researchers should begin by identifying the actual AI-assisted activities anticipated in the project and the obligations that could be affected.
Contractual provisions should then be developed with the appropriate institutional offices rather than copied indiscriminately from a generic template.
A simple decision framework
If AI use is limited to low-risk activities already covered by institutional policy
Consider whether a reference to applicable policies and an agreed operational protocol provides sufficient clarity.
If AI will process confidential or personal research information
Obtain appropriate privacy, ethics, security, and contractual review before authorizing processing.
If AI contributes to consequential analysis or interpretation
Specify verification responsibilities, decision authority, and relevant documentation requirements.
If collaborators have conflicting AI policies
Establish a mutually acceptable arrangement that complies with the obligations applicable to each partner.
If the project involves valuable intellectual property
Seek qualified legal review of relevant service terms, disclosure risks, and ownership arrangements.
Develop an AI-Use Schedule or Protocol
For collaborations with substantial AI activity, a separate schedule or protocol may be easier to maintain than placing every technical detail in the principal agreement.
Such a document can identify approved activities, authorized tools, restricted data categories, review responsibilities, and change-control procedures.
Its legal effect depends on how it is incorporated into the agreement and approved by the relevant parties.
Identify Who Can Approve Exceptions
Researchers should know whether proposed changes require approval from the principal investigator, institutional research office, data-protection personnel, ethics committee, or authorized contracting representatives.
Approval responsibilities should reflect the issue being considered.
A principal investigator may be able to approve a routine analytical workflow but may not have authority to waive contractual confidentiality restrictions or institutional ethics requirements.
Make the Agreement Operationally Usable
Contractual language should translate into procedures that researchers can follow.
For example, a prohibition on unauthorized disclosure should be accompanied by guidance on which data may be entered into approved AI services.
Similarly, a requirement for human verification should identify which outputs require review and who is responsible.
Unclear obligations may be difficult to implement even when the agreement appears comprehensive.
Obtain Appropriate Institutional Review
Research collaboration agreements can create binding legal obligations. Researchers should involve authorized institutional personnel when drafting or modifying contractual provisions.
Legal, privacy, research ethics, information-security, and technology-transfer expertise may be relevant depending on the project.
The objective is to establish enforceable and practical expectations without introducing provisions that conflict with existing agreements or applicable law.
