A Modularized Framework for Explaining Black Box Classifiers for Text Data
Bibliographic record
Abstract
The cumbersome amount of textual data produced in social media and in the new digital life makes the usage of automatic decision systems necessary for acting on text.The most widely adopted natural language processing approaches guarantee high accuracy but are black-box systems, that hide the logic of their internal decision processes.Since in various applications there is the need to unveil the reasons for the classification of different texts, the urge to explain black-box behaviour is growing among scientists.Thus, we propose a local model-agnostic method for interpreting text classifiers.Our method explains the decision of a text classifier on a given document by generating similar samples in its vicinity.The new samples are generated by replacing words of the document under analysis with their synonyms, antonyms, hyponyms, hypernyms, and definitions.Finally, these synthetic texts are used to train a decision tree that enables the user to identify important words explaining the classification outcome.An inspection of the synthetic documents generated by our proposal together with a set of words appropriately highlighted explain why the black box assigns a certain label to a given document.Deep and wide experimentation on various datasets and classifiers shows the effectiveness of our proposal and that its performance overcomes state-of-the-art methods.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".