Personalization in Human-AI Teams: Improving the Compatibility-Accuracy\n Tradeoff
Bibliographic record
Abstract
AI systems that model and interact with users can update their models over\ntime to reflect new information and changes in the environment. Although these\nupdates may improve the overall performance of the AI system, they may actually\nhurt the performance with respect to individual users. Prior work has studied\nthe trade-off between improving the system's accuracy following an update and\nthe compatibility of the updated system with prior user experience. The more\nthe model is forced to be compatible with a prior version, the higher loss in\naccuracy it will incur. In this paper, we show that by personalizing the loss\nfunction to specific users, in some cases it is possible to improve the\ncompatibility-accuracy trade-off with respect to these users (increase the\ncompatibility of the model while sacrificing less accuracy). We present\nexperimental results indicating that this approach provides moderate\nimprovements on average (around 20%) but large improvements for certain users\n(up to 300%).\n
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".