AAAI Paper "explainable ai" Papers

12 papers found

Filters:AAAIpaperexplainable aiClear all

Conference

AAAI 2025 (3,028)CVPR 2025 (2,873)ICCV 2025 (2,701)ICLR 2025 (3,827)ICML 2025 (3,340)ISMAR 2025 (229)NEURIPS 2025 (5,858)AAAI 2024 (2,289)CVPR 2024 (2,716)ECCV 2024 (2,387)ICLR 2024 (2,297)ICML 2024 (2,635)

Paper Type

poster (24,624)paper (8,140)oral (1,594)spotlight (1,421)highlight (975)

Constructing Fair Latent Space for Intersection of Fairness and Explainability

Hyungjun Joo, Hyeonggeun Han, Sehwan Kim et al.

AAAI 2025paperarXiv:2412.17523
2
citations

NOMATTERXAI: Generating “No Matter What” Alterfactual Examples for Explaining Black-Box Text Classification Models

Tuc Van Nguyen, James Michels, Hua Shen et al.

AAAI 2025paperarXiv:2408.10528
1
citations

Accelerating the Global Aggregation of Local Explanations

Alon Mor, Yonatan Belinkov, Benny Kimelfeld

AAAI 2024paperarXiv:2312.07991
6
citations

Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles

Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer et al.

AAAI 2024paperarXiv:2401.12069
30
citations

CGS-Mask: Making Time Series Predictions Intuitive for All

Feng Lu, Wei Li, Yifei Sun et al.

AAAI 2024paperarXiv:2312.09513
1
citations

Enhance Sketch Recognition’s Explainability via Semantic Component-Level Parsing

Guangming Zhu, Siyuan Wang, Tianci Wu et al.

AAAI 2024paperarXiv:2312.07875
2
citations

Faithful Model Explanations through Energy-Constrained Conformal Counterfactuals

Patrick Altmeyer, Mojtaba Farmanbar, Arie Van Deursen et al.

AAAI 2024paperarXiv:2312.10648
7
citations

Gaussian Process Neural Additive Models

Wei Zhang, Brian Barr, John Paisley

AAAI 2024paperarXiv:2402.12518
11
citations

Keep the Faith: Faithful Explanations in Convolutional Neural Networks for Case-Based Reasoning

Tom Nuno Wolf, Fabian Bongratz, Anne-Marie Rickmann et al.

AAAI 2024paperarXiv:2312.09783
8
citations

Learning Performance Maximizing Ensembles with Explainability Guarantees

Vincent Pisztora, Jia Li

AAAI 2024paperarXiv:2312.12715

Towards More Faithful Natural Language Explanation Using Multi-Level Contrastive Learning in VQA

Chengen Lai, Shengli Song, Shiqi Meng et al.

AAAI 2024paperarXiv:2312.13594
9
citations

Using Stratified Sampling to Improve LIME Image Explanations

Muhammad Rashid, Elvio G. Amparore, Enrico Ferrari et al.

AAAI 2024paperarXiv:2403.17742
7
citations