v1v2 (latest)

Assessing the Local Interpretability of Machine Learning Models

9 February 2019

Papers citing "Assessing the Local Interpretability of Machine Learning Models"

26 / 26 papers shown

Title
Towards Human-centered Design of Explainable Artificial Intelligence (XAI): A Survey of Empirical Studies Shuai Ma 89 1 0 28 Oct 2024
OpenHEXAI: An Open-Source Framework for Human-Centered Evaluation of Explainable Machine Learning Jiaqi Ma Vivian Lai Yiming Zhang Chacha Chen Paul Hamilton Davor Ljubenkov Himabindu Lakkaraju Chenhao Tan ELM 46 3 0 20 Feb 2024
Overconfident and Unconfident AI Hinder Human-AI Collaboration Jingshu Li Yitian Yang Renwen Zhang Yi-Chieh Lee 76 1 0 12 Feb 2024
Q-SENN: Quantized Self-Explaining Neural Networks Thomas Norrenbrock Marco Rudolph Bodo Rosenhahn FAtt AAML MILM 102 7 0 21 Dec 2023
A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When? M. Rubaiyat Hossain Mondal Prajoy Podder 91 64 0 10 Apr 2023
Take 5: Interpretable Image Classification with a Handful of Features Thomas Norrenbrock Marco Rudolph Bodo Rosenhahn FAtt 84 7 0 23 Mar 2023
Selective Explanations: Leveraging Human Input to Align Explainable AI Vivian Lai Yiming Zhang Chacha Chen Q. V. Liao Chenhao Tan 95 45 0 23 Jan 2023
Evaluation and Improvement of Interpretability for Self-Explainable Part-Prototype Networks Qihan Huang Mengqi Xue Wenqi Huang Haofei Zhang Mingli Song Yongcheng Jing Mingli Song AAML 74 28 0 12 Dec 2022
Why we do need Explainable AI for Healthcare Giovanni Cina Tabea E. Rober Rob Goedhart Ilker Birbil 71 14 0 30 Jun 2022
Interpretation Quality Score for Measuring the Quality of interpretability methods Sean Xie Soroush Vosoughi Saeed Hassanpour XAI 111 5 0 24 May 2022
Machine Explanations and Human Understanding Chacha Chen Shi Feng Amit Sharma Chenhao Tan 92 26 0 08 Feb 2022
Rethinking Explainability as a Dialogue: A Practitioner's Perspective Himabindu Lakkaraju Dylan Slack Yuxin Chen Chenhao Tan Sameer Singh LRM 110 64 0 03 Feb 2022
Towards a Science of Human-AI Decision Making: A Survey of Empirical Studies Vivian Lai Chacha Chen Q. V. Liao Alison Smith-Renner Chenhao Tan 123 188 0 21 Dec 2021
Opportunities for Machine Learning to Accelerate Halide Perovskite Commercialization and Scale-Up Rishi E. Kumar A. Tiihonen Shijing Sun D. Fenning Zhe Liu Tonio Buonassisi 35 11 0 08 Oct 2021
A Review of Explainable Artificial Intelligence in Manufacturing G. Sofianidis Jože M. Rožanec Dunja Mladenić D. Kyriazis 84 17 0 05 Jul 2021
On the Interaction of Belief Bias and Explanations Ana Valeria González Anna Rogers Anders Søgaard FAtt 80 19 0 29 Jun 2021
Interpretable Deep Learning: Interpretation, Interpretability, Trustworthiness, and Beyond Xuhong Li Haoyi Xiong Xingjian Li Xuanyu Wu Xiao Zhang Ji Liu Jiang Bian Dejing Dou AAML FaML XAI HAI 82 341 0 19 Mar 2021
Evaluating the Interpretability of Generative Models by Interactive Reconstruction A. Ross Nina Chen Elisa Zhao Hang Elena L. Glassman Finale Doshi-Velez 158 49 0 02 Feb 2021
Explainable Artificial Intelligence Approaches: A Survey Sheikh Rabiul Islam W. Eberle S. Ghafoor Mohiuddin Ahmed XAI 87 104 0 23 Jan 2021
Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges Christoph Molnar Giuseppe Casalicchio B. Bischl AI4TS AI4CE 114 405 0 19 Oct 2020
The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategies A. Markus J. Kors P. Rijnbeek 91 471 0 31 Jul 2020
Towards Quantification of Explainability in Explainable Artificial Intelligence Methods Sheikh Rabiul Islam W. Eberle S. Ghafoor XAI 82 43 0 22 Nov 2019
Fairness Warnings and Fair-MAML: Learning Fairly with Minimal Data Dylan Slack Sorelle A. Friedler Emile Givental FaML 118 55 0 24 Aug 2019
Machine Learning Testing: Survey, Landscapes and Horizons Jie M. Zhang Mark Harman Lei Ma Yang Liu VLM AILaw 110 756 0 19 Jun 2019
Proposed Guidelines for the Responsible Use of Explainable Machine Learning Patrick Hall Navdeep Gill N. Schmidt SILM XAI FaML 77 29 0 08 Jun 2019
Quantifying Model Complexity via Functional Decomposition for Better Post-Hoc Interpretability Christoph Molnar Giuseppe Casalicchio B. Bischl FAtt 54 60 0 08 Apr 2019