Analyzing XAI Metrics: Summary of the Literature Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The widespread adoption of Artificial Intelligence (AI) models by various industries in recent years have made Explainable Artificial Intelligence (XAI) an active field of research. This adoption can cause trust and effectiveness to suffer if the results of these models are not favorable in some way. XAI has advanced to the point where many metrics have been proposed as reasons for the outputs of many AI models. However, there is little consensus about what technical metrics are most important, nor is there a consensus on how best to analyze explainable methods and models. A discussion of varying attempts at this is brought forth, but the paper also goes into the ethics of AI and its societal impact. Given the modern ubiquity with which AI exists and the immensely multidisciplinary approach, AI has evolved into, using only technical metrics cannot fully describe XAI’s effectiveness. This paper explores several approaches to measuring the ethical effects of XAI, whether it has any bearing on modern research, as well as how the impacts of AI and XAI are measured on society. The full attempt at quantifying XAI models’ effectiveness is explored from a technical and non-technical point of view.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it