Stop ordering machine learning algorithms by their explainability! A
user-centered investigation of performance and explainability

Stop ordering machine learning algorithms by their explainability! A user-centered investigation of performance and explainability

20 June 2022

Christian Janiesch

Papers citing "Stop ordering machine learning algorithms by their explainability! A user-centered investigation of performance and explainability"

16 / 16 papers shown

Title
Integrating Explainable AI in Medical Devices: Technical, Clinical and Regulatory Insights and Recommendations Dima Alattal Asal Khoshravan Azar P. Myles Richard Branson Hatim Abdulhussein Allan Tucker 29 0 0 10 May 2025
Engineering the Law-Machine Learning Translation Problem: Developing Legally Aligned Models Mathias Hanson Gregory Lewkowicz Sam Verboven AILaw ELM 69 1 0 23 Apr 2025
Beware of "Explanations" of AI David Martens Galit Shmueli Theodoros Evgeniou Kevin Bauer Christian Janiesch ... Claudia Perlich Wouter Verbeke Alona Zharova Patrick Zschech F. Provost 21 0 0 09 Apr 2025
Constrained Neural Networks for Interpretable Heuristic Creation to Optimise Computer Algebra Systems Dorian Florescu Matthew England 16 1 0 26 Apr 2024
Detecting Brain Tumors through Multimodal Neural Networks Antonio Curci Andrea Esposito 11 4 0 10 Jan 2024
Is Machine Learning Unsafe and Irresponsible in Social Sciences? Paradoxes and Reconsidering from Recidivism Prediction Tasks Jianhong Liu D. Li 8 1 0 11 Nov 2023
An Introduction to Natural Language Processing Techniques and Framework for Clinical Implementation in Radiation Oncology Reza Khanmohammadi Mohammad Mahdi Ghassemi Kyle Verdecchia A. Ghanem Luo Bing ... H. Bagher-Ebadian Farzan Siddiqui Mohamed Elshaikh B. Movsas Kundan Thind LM&MA 28 2 0 03 Nov 2023
Transitivity Recovering Decompositions: Interpretable and Robust Fine-Grained Relationships Abhra Chaudhuri Massimiliano Mancini Zeynep Akata Anjan Dutta 21 2 0 24 Oct 2023
QXAI: Explainable AI Framework for Quantitative Analysis in Patient Monitoring Systems T. Shaik Xiaohui Tao Haoran Xie Lin Li Juan D. Velásquez Niall Higgins 30 2 0 19 Sep 2023
Predicting and explaining nonlinear material response using deep Physically Guided Neural Networks with Internal Variables Javier Orera-Echeverria J. Ayensa-Jiménez Manuel Doblaré 18 1 0 07 Aug 2023
Explainable AI Insights for Symbolic Computation: A case study on selecting the variable ordering for cylindrical algebraic decomposition Lynn Pickering Tereso Del Rio Almajano Matthew England Kelly Cohen 16 12 0 24 Apr 2023
Impact Of Explainable AI On Cognitive Load: Insights From An Empirical Study L. Herm 15 22 0 18 Apr 2023
Hypothesis Testing and Machine Learning: Interpreting Variable Effects in Deep Artificial Neural Networks using Cohen's f2 Wolfgang Messner CML 13 12 0 02 Feb 2023
Explainable Human-in-the-loop Dynamic Data-Driven Digital Twins Nan Zhang Rami Bahsoon Nikos Tziritas Georgios Theodoropoulos 29 9 0 19 Jul 2022
A Survey on Bias and Fairness in Machine Learning Ninareh Mehrabi Fred Morstatter N. Saxena Kristina Lerman Aram Galstyan SyDa FaML 323 4,203 0 23 Aug 2019
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 251 3,683 0 28 Feb 2017