Responsible <scp>AI</scp> : Fostering Ethical and Inclusive Information Ecosystems
Bibliographic record
Abstract
ABSTRACT Six interdisciplinary researchers will present their past and ongoing research projects with diverse and vulnerable communities, demonstrating the significance, innovative methodologies, and outcomes of designing and developing AI systems responsibly. Panelists' research at the intersection of Generative AI, algorithms, trust, ethics, values, bias, privacy, accountability, transparency, information equity, and diversity will spark a thought‐provoking and meaningful discussion among attendees. The panelists will share the relevant challenges, risks, precautions, and solutions (e.g., top‐down and bottom‐up strategies, novel methods, incremental vs. revolutionary practices). They will encourage attendees to reflect on their perspectives and experiences on the topics, including but not limited to the roles of stakeholders (e.g., technology vendors developing AI systems, governments) in designing and developing responsible AI systems and subsequent theoretical and practical implications for individuals (e.g., journalists, information professionals, rural youth, university students), organizations (e.g., fintech, hospitals, schools, libraries, universities, United Nations), and society (e.g., developed and developing countries).
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.048 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.028 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".