| Website | https://augmanitai.com |
| ORCID | https://orcid.org/0009-0006-3773-7796 |
| DOI | https://doi.org/10.5281/zenodo.14984941 |
Why do we have precise terms for LLM failures like "hallucination" but almost none for the human side of AI interaction?
The AUGMANITAI framework addresses this gap — a terminology compendium identifying and naming phenomena that occur when humans interact with AI systems. From sycophancy patterns to confidence calibration artifacts.
Open-access, DOI-published, CC BY-NC-ND 4.0.
doi.org/10.5281/zenodo.14984941
#AI #NLP #HumanAI #Terminology #OpenScience #LLM #AUGMANITAI
When an LLM confidently presents false information, researchers call it "hallucination." When it agrees with everything you say, the term is "sycophancy." But most phenomena in human-AI interaction still have no name.
AUGMANITAI is an open-access compendium of 1,000+ terms for human-AI interaction. ISO-inspired terminology science, DOI-published on Zenodo (CC BY-NC-ND 4.0).
With increasing focus on open research, the Concordat of Open Research Data was created in 2016, laying out 10 principles for embedding open data practices in United Kingdom (UK) academic research. The Concordat’s principles relate to ethical, legal and professional obligations, addressing areas such as practicality, affordability, transparency, robustness and fairness, mechanisms and infrastructure, data integrity, citation and attribution, aiming for all research data to be ‘as open as possible, as closed as necessary’. The STAR (Sustainable & TrAnsparent Research data) project, is led by the UK Reproducibility Network (UKRN), and supported by several UK research institutions, with contributions from the Data Curation Centre and other sector experts. The project employs qualitative methods to evaluate the implementation of the Concordat’s principles in UK research institutions since inception. This was done through interviews, focus groups, and workshops we held with 43 research support staff across 20 UK universities. In this paper, we report on key learnings from the STAR project, including progress and barriers to open data; and ways in which institutions are and could better be supported in the curation, publication, and reuse of open data.