Interpretable to Whom? A Role-based Model for Analyzing Interpretable
Machine Learning Systems

Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems

20 June 2018

Richard J. Tomsett

Daniel Harborne

Supriyo Chakraborty

Papers citing "Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems"

13 / 13 papers shown

Title
A Mechanistic Explanatory Strategy for XAI Marcin Rabiza 51 1 0 02 Nov 2024
How should AI decisions be explained? Requirements for Explanations from the Perspective of European Law Benjamin Frész Elena Dubovitskaya Danilo Brajovic Marco F. Huber Christian Horz 49 7 0 19 Apr 2024
The Case Against Explainability Hofit Wasserman Rozen N. Elkin-Koren Ran Gilad-Bachrach AILaw ELM 26 1 0 20 May 2023
Flexible and Inherently Comprehensible Knowledge Representation for Data-Efficient Learning and Trustworthy Human-Machine Teaming in Manufacturing Environments Vedran Galetić Alistair Nottle 27 1 0 19 May 2023
The Influence of Explainable Artificial Intelligence: Nudging Behaviour or Boosting Capability? Matija Franklin TDI 23 1 0 05 Oct 2022
Post-Hoc Explanations Fail to Achieve their Purpose in Adversarial Contexts Sebastian Bordt Michèle Finck Eric Raidl U. V. Luxburg AILaw 39 77 0 25 Jan 2022
On Two XAI Cultures: A Case Study of Non-technical Explanations in Deployed AI System Helen Jiang Erwen Senge 25 7 0 02 Dec 2021
The Who in XAI: How AI Background Shapes Perceptions of AI Explanations Upol Ehsan Samir Passi Q. V. Liao Larry Chan I-Hsiang Lee Michael J. Muller Mark O. Riedl 32 85 0 28 Jul 2021
Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needs Harini Suresh Steven R. Gomez K. Nam Arvind Satyanarayan 34 126 0 24 Jan 2021
How can I choose an explainer? An Application-grounded Evaluation of Post-hoc Explanations Sérgio Jesus Catarina Belém Vladimir Balayan João Bento Pedro Saleiro P. Bizarro João Gama 136 119 0 21 Jan 2021
Explainable Artificial Intelligence for Process Mining: A General Overview and Application of a Novel Local Explanation Approach for Predictive Process Monitoring Nijat Mehdiyev Peter Fettke AI4TS 25 55 0 04 Sep 2020
A Performance-Explainability Framework to Benchmark Machine Learning Methods: Application to Multivariate Time Series Classifiers Kevin Fauvel Véronique Masson Elisa Fromont AI4TS 44 17 0 29 May 2020
Techniques for Interpretable Machine Learning Mengnan Du Ninghao Liu Xia Hu FaML 33 1,071 0 31 Jul 2018