Rethinking responsible AI from ethical pillars to sociotechnical practice
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
Abstract The growing demand for Responsible AI has crystallised around normative principles: fairness, transparency, accountability, privacy, safety, and value alignment, yet their implementation often reveals profound conceptual and operational instability. This research employs a constructively critical approach to examine the structural tensions underlying these pillars and argues that prevailing frameworks treat responsibility as a static compliance exercise, detached from the sociotechnical realities of AI systems. Drawing on traditions in process ethics, participatory design, and adaptive governance, the study develops a reframed understanding of Responsible AI as a dynamic, negotiated, and context-sensitive process. It advances a composite theoretical model and a layered ecosystem framework that redistributes responsibility across design, deployment, governance, and public deliberation. Through this reframing, the work offers both a critique of the dominant paradigm and a practical roadmap for interdisciplinary engagement, ethical responsiveness, and institutional reflexivity. The contribution is twofold: a conceptual synthesis that challenges the assumptions of checklist ethics, and an applied methodology with implications for AI researchers, developers, policymakers, and civil society actors working to navigate the ethical complexity of real-world AI design, deployment and use.
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 | 0.011 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.003 | 0.007 |
| 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