Navigating accountability: the role of paradata in AI documentation and governance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The increased use of Artificial Intelligence (AI) has prompted governments internationally to provide guidance and legislation to maximize the benefits of AI while minimizing the risks to humans and organizations. This paper explores how published requirements for documentation in a sampling of authoritative texts address the challenges of creating, capturing and preserving records of the design, implementation and use of AI tools for accountability and transparency, and how the analytical concept of paradata can help to meet the recordkeeping challenges presented by the design, development and implementation of AI systems. Design/methodology/approach Inductive reading and conceptual analysis of a set of AI laws, regulations and frameworks published by the EU, UK, USA, Canada and Singapore. Findings The authoritative texts reviewed clearly describe activities which imply the necessity of records creation and preservation. Identifying specific documents necessary to comprise a sufficient body of records to provide evidence of accountable AI implementation and operation can be difficult. Literature on paradata in archival applications of AI may prove productive in identifying relevant information artifacts for preservation in the AI process. Paradata is produced by those designing and implementing AI systems and by AI systems themselves. Practical implications Identifying relevant paradata produced by AI systems requires archivists to develop both the capacity to analyze and the vocabulary to discuss these systems in order to preserve evidence of their operation in compliance with legislation and international standards. Originality/value No comparable comparative analyses have been published in the archives and information field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it