- Groundwork
The sIfA tool for a Statement of Intellectual Fellowship and Accountability: An invitation to reflect on how humans and AI interact in producing knowledge
- May 20, 2026
- 10:37 am
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PROJECT TYPE
Location
BEHAVIORAL THEME
OVERVIEW
The sIfA tool is available for download here and on GitHub.
The sIfA tool is a browser-based, open-source software designed to support transparent reflection on how humans and AI interact in producing knowledge. Built around the 14 CRediT contributor roles maintained by NISO, the tool enables users to declare AI involvement across different stages of research and generate both a structured contribution table and a single-page visualisation known as the “sIfA figure.” It also supports user-defined taxonomies, making it adaptable for publications, institutional reporting, teaching, and broader knowledge production workflows.
Questions to consider:
- How can researchers transparently document interactions between humans and AI in knowledge production?
- What standards or frameworks are needed for meaningful AI disclosure in research?
- How can AI contribution statements support accountability, inclusivity, and collective learning?
- What role can visual and machine-readable tools play in shaping future norms around AI-assisted research?
The Groundwork combines literature review, reflective analysis, and applied software development to explore emerging debates on AI disclosure and research accountability. The sIfA tool was developed as an open-source browser-based application that guides users through contributor roles, levels of participation, extent of AI interaction, audit trails, and AI tool usage. The system generates exportable tables and visual summaries that can accompany publications, reports, and institutional disclosures.
THEMATIC AREAS
Key Findings:
- AI is rapidly reshaping knowledge production, but norms for transparency and disclosure remain underdeveloped.
- Researchers often underreport AI use due to uncertainty, reputational concerns, and lack of shared standards.
- Existing contribution frameworks like CRediT provide a strong foundation for structured AI accountability statements.
- Reflection and transparency about human/AI interaction can strengthen research integrity, inclusivity, and accountability.
- The sIfA tool enables machine-readable, standardized, and visually accessible declarations of AI involvement in research processes.
- Human accountability remains central, regardless of the extent of AI interaction.
Implications for Policy or Development
- Research institutions and publishers should adopt clearer standards for AI disclosure and accountability.
- Transparent AI contribution statements can strengthen trust, integrity, and reflexivity in knowledge production.
- Open, machine-readable documentation of AI use can support future research on evolving scholarly practices.
- The sIfA approach offers a scalable framework for balancing innovation with human responsibility in AI-assisted work.
- Broader adoption of tools like sIfA could contribute to more equitable and inclusive recognition of contributions in research and collaborative knowledge systems.