Papers on “model interpretability”
Research on “model interpretability” in the 480 tracked journals: papers per year and category, the journals, institutions and countries publishing it, the keywords studied with it and its most-cited papers. It is not among the most frequent keywords of the last 30 days, so this page has no list of its latest papers. Also as Markdown.
← Top research keywords · Keyword trends
Key findings#
- From 2017 to 2026 (so far), the keyword “model interpretability” appeared in 926 papers in the tracked journals, most of them in AI (623), operations research (97) and finance (52).
- 220 of them were published in 2025, against 10 in 2017.
- Since 2023, the institutions with the most of these papers were Chinese Academy of Sciences (14), Shanghai Jiao Tong University (13) and University of Electronic Science and Technology of China (12).
- The journals publishing most of them were Expert Systems with Applications (89), Neural Computing and Applications (47) and Knowledge-Based Systems (37).
- The most-cited of them is “Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead” (Nature Machine Intelligence, 2019), with 10,516 citations.
Papers per year#
20172026 (so far)
| Category | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 | 2025 | 2026 (so far) |
|---|---|---|---|---|---|---|---|---|---|---|
| AI | 10 | 5 | 17 | 29 | 47 | 67 | 80 | 99 | 144 | 125 |
| Operations Research | 0 | 2 | 2 | 3 | 7 | 7 | 12 | 24 | 23 | 17 |
| Finance | 0 | 0 | 0 | 1 | 3 | 5 | 10 | 8 | 12 | 13 |
| Information Systems | 0 | 0 | 1 | 3 | 4 | 1 | 6 | 11 | 8 | 12 |
| Economics | 0 | 1 | 1 | 3 | 2 | 2 | 0 | 6 | 13 | 11 |
| Statistics | 0 | 0 | 0 | 0 | 3 | 2 | 5 | 8 | 6 | 6 |
| Management | 0 | 0 | 1 | 1 | 1 | 3 | 3 | 1 | 8 | 3 |
| Higher Education and Research | 0 | 0 | 0 | 0 | 0 | 3 | 1 | 1 | 0 | 1 |
| Tourism and Hospitality | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 3 | 1 |
| Accounting | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 |
| Health Economics and Policy | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 |
| Marketing | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| All categories | 10 | 8 | 22 | 41 | 67 | 90 | 117 | 161 | 220 | 190 |
Where the papers come from#
| # | Journal | Papers |
|---|---|---|
| 1 | Expert Systems with Applications | 89 |
| 2 | Neural Computing and Applications | 47 |
| 3 | Knowledge-Based Systems | 37 |
| 4 | Applied Intelligence | 36 |
| 5 | Artificial Intelligence Review | 32 |
| 6 | Annals of Operations Research | 18 |
| 7 | Pattern Recognition | 17 |
| 8 | Information Fusion | 17 |
| 9 | Neural Networks | 16 |
| 10 | Data Mining and Knowledge Discovery | 16 |
| # | Institution | Papers |
|---|---|---|
| 1 | Chinese Academy of Sciences | 14 |
| 2 | Shanghai Jiao Tong University | 13 |
| 3 | University of Electronic Science and Technology of China | 12 |
| 4 | Hong Kong Polytechnic University | 10 |
| 5 | Sichuan University | 10 |
| 6 | University of Science and Technology of China | 10 |
| 7 | Zhejiang University | 10 |
| 8 | Friedrich-Alexander-Universität Erlangen-Nürnberg | 8 |
| 9 | Centre National de la Recherche Scientifique | 7 |
| 10 | Mansoura University | 7 |
| # | Country | Papers |
|---|---|---|
| 1 | China | 259 |
| 2 | United States | 105 |
| 3 | United Kingdom | 49 |
| 4 | Australia | 42 |
| 5 | Germany | 40 |
| 6 | India | 37 |
| 7 | Italy | 31 |
| 8 | Canada | 27 |
| 9 | Hong Kong | 26 |
| 10 | South Korea | 25 |
| # | Keyword | Papers |
|---|---|---|
| 1 | explainable AI | 136 |
| 2 | SHAP | 90 |
| 3 | feature selection | 48 |
| 4 | feature importance | 44 |
| 5 | random forest | 41 |
| 6 | class imbalance | 39 |
| 7 | lime | 38 |
| 8 | XGBoost | 31 |
| 9 | black-box models | 29 |
Most-cited papers#
| # | Citations | Per month | Title | Journal | Published | Authors |
|---|---|---|---|---|---|---|
| 1 | 10,516 | 117.2 | Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead | Nature Machine Intelligence | 2019-05-13 | Cynthia D Rudin |
| 2 | 2,214 | 30.9 | Shortcut learning in deep neural networks | Nature Machine Intelligence | 2020-11-10 | Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis et al. |
| 3 | 2,136 | 55.7 | Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence | Cognitive Computation | 2023-08-24 | Vikas Hassija, Vinay Chamola, Atmesh Mahapatra et al. |
| 4 | 1,302 | 16.8 | An interpretable mortality prediction model for COVID-19 patients | Nature Machine Intelligence | 2020-05-14 | Yan Li, Hai‐Tao Zhang, Jorge M. Gonçalves et al. |
| 5 | 770 | 6.6 | A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring | Expert Systems with Applications | 2017-02-10 | Yufei Xia, Chuanzhe Liu, Yuying Li et al. |
Cite this page
Scholar4AI. (2026). Papers on “model interpretability”. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/keywords/model-interpretability
@misc{scholar4ai-keywords-model-interpretability,
author = {{Scholar4AI}},
title = {Papers on “model interpretability”},
year = {2026},
howpublished = {\url{https://scholar4ai.com/keywords/model-interpretability}},
urldate = {2026-11-03},
note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}