Cover Image

xai-cola: A Python library for sparsifying counterfactual explanations

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhu, Lin, You, Lei
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Artificial Intelligence Computers and Society
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

FairSHAP: Preprocessing for Fairness Through Attribution-Based Data Augmentation
by: Zhu, Lin, et al.
Published: (2025)

Towards plausibility in time series counterfactual explanations
by: Kostrzewa, Marcin, et al.
Published: (2026)

In defence of post-hoc explanations in medical AI
by: Hatherley, Joshua, et al.
Published: (2025)

Actionable and diverse counterfactual explanations incorporating domain knowledge and plausibility constraints
by: Bobek, Szymon, et al.
Published: (2025)

HyConEx: Hypernetwork classifier with counterfactual explanations for tabular data
by: Marszałek, Patryk, et al.
Published: (2025)

FairHealth: An Open-Source Python Library for Trustworthy Healthcare AI in Low-Resource Settings
by: Yesmin, Farjana
Published: (2026)

The future of human-centric eXplainable Artificial Intelligence (XAI) is not post-hoc explanations
by: Swamy, Vinitra, et al.
Published: (2023)

Guiding the generation of counterfactual explanations through temporal background knowledge for Predictive Process Monitoring
by: Buliga, Andrei, et al.
Published: (2024)

A multi-criteria approach for selecting an explanation from the set of counterfactuals produced by an ensemble of explainers
by: Stępka, Ignacy, et al.
Published: (2024)

LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use Cases
by: Bouchard, Dylan, et al.
Published: (2025)

Local Universal Explainer (LUX) -- a rule-based explainer with factual, counterfactual and visual explanations
by: Bobek, Szymon, et al.
Published: (2023)

Simulating counterfactuals
by: Karvanen, Juha, et al.
Published: (2023)

How Far Are We From AGI: Are LLMs All We Need?
by: Feng, Tao, et al.
Published: (2024)

xai_evals : A Framework for Evaluating Post-Hoc Local Explanation Methods
by: Seth, Pratinav, et al.
Published: (2025)

AI Toolkit: Libraries and Essays for Exploring the Technology and Ethics of AI
by: Ho, Levin, et al.
Published: (2025)

Programmable Virtual Humans Toward Human Physiologically-Based Drug Discovery
by: Wu, You, et al.
Published: (2025)

Can a large language model be a gaslighter?
by: Li, Wei, et al.
Published: (2024)

Learning Fair Models without Sensitive Attributes: A Generative Approach
by: Zhu, Huaisheng, et al.
Published: (2022)

IoTCO2: Assessing the End-To-End Carbon Footprint of Internet-of-Things-Enabled Deep Learning
by: Chen, Fan, et al.
Published: (2024)

Supervised Algorithmic Fairness in Distribution Shifts: A Survey
by: Shao, Minglai, et al.
Published: (2024)

3DG: A Framework for Using Generative AI for Handling Sparse Learner Performance Data From Intelligent Tutoring Systems
by: Zhang, Liang, et al.
Published: (2024)

OpenCity: Open Spatio-Temporal Foundation Models for Traffic Prediction
by: Li, Zhonghang, et al.
Published: (2024)

Multi-Output Distributional Fairness via Post-Processing
by: Li, Gang, et al.
Published: (2024)

Beyond Prompting: An Efficient Embedding Framework for Open-Domain Question Answering
by: Hu, Zhanghao, et al.
Published: (2025)

LLM Safety Alignment is Divergence Estimation in Disguise
by: Haldar, Rajdeep, et al.
Published: (2025)

Incorporating Graph Attention Mechanism into Geometric Problem Solving Based on Deep Reinforcement Learning
by: Zhong, Xiuqin, et al.
Published: (2024)

Transparent AI: The Case for Interpretability and Explainability
by: Ramachandram, Dhanesh, et al.
Published: (2025)

Towards Counterfactual Fairness-aware Domain Generalization in Changing Environments
by: Lin, Yujie, et al.
Published: (2023)

Trajectory Data Management and Mining: A Survey from Deep Learning to the LLM Era
by: Chen, Wei, et al.
Published: (2024)

FrontierScience: Evaluating AI's Ability to Perform Expert-Level Scientific Tasks
by: Wang, Miles, et al.
Published: (2026)

AdvKT: An Adversarial Multi-Step Training Framework for Knowledge Tracing
by: Fu, Lingyue, et al.
Published: (2025)

Unmasking and Improving Data Credibility: A Study with Datasets for Training Harmless Language Models
by: Zhu, Zhaowei, et al.
Published: (2023)

Rigor in AI: Doing Rigorous AI Work Requires a Broader, Responsible AI-Informed Conception of Rigor
by: Olteanu, Alexandra, et al.
Published: (2025)

Integrating LSTM and BERT for Long-Sequence Data Analysis in Intelligent Tutoring Systems
by: Li, Zhaoxing, et al.
Published: (2024)

LLMCO2: Advancing Accurate Carbon Footprint Prediction for LLM Inferences
by: Fu, Zhenxiao, et al.
Published: (2024)

Transformers in Healthcare: A Survey
by: Nerella, Subhash, et al.
Published: (2023)

Optimal rates for density and mode estimation with expand-and-sparsify representations
by: Sinha, Kaushik, et al.
Published: (2026)

Creativity and Machine Learning: A Survey
by: Franceschelli, Giorgio, et al.
Published: (2021)

A Collectivist, Economic Perspective on AI
by: Jordan, Michael I.
Published: (2025)

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata
by: Tertulino, Rodrigo, et al.
Published: (2025)