Assessing the Local Interpretability of Machine Learning Models

9 February 2019

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

10 / 10 papers shown

Title
Overconfident and Unconfident AI Hinder Human-AI Collaboration Jingshu Li Yitian Yang Renwen Zhang Yi-Chieh Lee 45 1 0 12 Feb 2024
A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When? M. Rubaiyat Hossain Mondal Prajoy Podder 28 56 0 10 Apr 2023
Take 5: Interpretable Image Classification with a Handful of Features Thomas Norrenbrock Marco Rudolph Bodo Rosenhahn FAtt 42 7 0 23 Mar 2023
Why we do need Explainable AI for Healthcare Giovanni Cina Tabea E. Rober Rob Goedhart Ilker Birbil 34 14 0 30 Jun 2022
Interpretation Quality Score for Measuring the Quality of interpretability methods Sean Xie Soroush Vosoughi Saeed Hassanpour XAI 21 5 0 24 May 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 33 186 0 21 Dec 2021
Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges Christoph Molnar Giuseppe Casalicchio B. Bischl AI4TS AI4CE 28 396 0 19 Oct 2020
Towards Quantification of Explainability in Explainable Artificial Intelligence Methods Sheikh Rabiul Islam W. Eberle S. Ghafoor XAI 22 42 0 22 Nov 2019
Fairness Warnings and Fair-MAML: Learning Fairly with Minimal Data Dylan Slack Sorelle A. Friedler Emile Givental FaML 32 54 0 24 Aug 2019
Machine Learning Testing: Survey, Landscapes and Horizons Jie M. Zhang Mark Harman Lei Ma Yang Liu VLM AILaw 39 741 0 19 Jun 2019