Navigating the Ethical Terrain of AI Technologies: A Practice-Based View
Bibliographic record
Abstract
Our symposium will explore the unique dynamics that give rise to and intensify ethical concerns with regards to AI technologies. The research presented will discuss topics such as how the design stage of an AI system cannot be isolated as an independent stage from the rest of the AI lifecycle. We will engage with the audience in meaningful discussion about the ethics of AI that is grounded both in the development stages as well as the use, implementation, and ongoing maintenance stages. The nascent field of AI ethics will greatly benefit from adopting a more processual and situated understanding of ethics with regards to AI tools and from empirically exploring the use of AI and its enacted ethical implications (Barley & Bailey, 2020). This symposium will provide a forum to explore current research in this area and foster discussions to enhance impact on both research and practice. I Know Best: The Constitution of Ethics in the Co-Development of AI across Professions Author: Yiran Xu; - Author: Rene Wiedner; Warwick Business School Author: Joe Nandhakumar; U. of Warwick Is This Fair? How Algorithmic Fairness Becomes Performative Author: Sarah Lebovitz; U. of Virginia Author: Emmanouil Gkeredakis; IESE Business School When Fairness Is at Stake: How Morality Shapes the Deployment of AI in Organizations Author: Elmira Van Den Broek; Stockholm School of Economics A Quality of Mercy is not Trained: Entanglement of AI and Ethics in Operating Room Scheduling Author: Samer Faraj; McGill U. Author: Anand Bhardwaj; McGill U. - Desautels Faculty of Management
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 | 0.092 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.022 | 0.138 |
| Scholarly communication | 0.035 | 0.036 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.019 | 0.032 |
| Insufficient payload (model declined to judge) | 0.004 | 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".