Explainability for All: Care Ethics for Implementing Artificial Intelligence
Bibliographic record
Abstract
As the Artificial Intelligence (AI) industry is growing, concerns are increasing regarding its ethical implications. Literature indicates that explainability or transparency, which is defined as the ability to understand and have knowledge about a system, is a top priority for organizations working with AI, given the inherently complex nature of these systems. Transparency allows increased functionality, accountability, trust, and knowledge of AI. Furthermore, prioritizing strong actor networks leads to a tighter-knit team of individuals and improves collaboration and responsibility within an organization. A case study of a company in the AI industry suggests that care ethics plays a critical role in ensuring the responsible and ethical implementation of best practices for transparency in an organization. Both internally with employees and externally with clients, taking responsibility is crucial for ensuring a strong network of ethical actors for an organization.
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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 | 0.083 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.065 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.019 | 0.019 |
| 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".