Bibliographic record
Abstract
Recommender systems are all around us; they can be found in news applications, YouTube,\nNetflix, the healthcare industry, and e-commerce. These recommender systems are\ninfluencing our choices and the information that is presented to us. This makes it crucial to\nthink about the ethical consequences of these recommendations and possible solutions to\nethical issues. In this thesis, we have identified the main ethical challenges of recommender\nsystems, and we looked at one specific, promising solution called the secondary ethical layer.\nThe secondary ethical layer is a general ethical filter which filters out any unethical\nrecommendations based on cultural and personal preferences while also taking into account\nall the different stakeholders on which recommendations can have an effect (such as the user,\nprovider, system and society). We have found that this solution can solve some ethical issues,\nspecifically with regards to inappropriate content, unfairness (biases) and issues for society. It\ndoes not solve problems such as the lack of opacity and some privacy issues within\nrecommender systems. This thesis identifies different key elements of the ethical layer and\ncreates the fundaments on which a practical solution can be built.
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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 | 0.028 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".