Interpretable Machine Learning: Moving From Mythos to Diagnostics

Interpretable Machine Learning: Moving From Mythos to Diagnostics

10 March 2021

Ameet Talwalkar

Papers citing "Interpretable Machine Learning: Moving From Mythos to Diagnostics"

13 / 13 papers shown

Title
Beyond Model Interpretability: Socio-Structural Explanations in Machine Learning Andrew Smart Atoosa Kasirzadeh 30 6 0 05 Sep 2024
Biathlon: Harnessing Model Resilience for Accelerating ML Inference Pipelines Chaokun Chang Eric Lo Chunxiao Ye 21 2 0 18 May 2024
What Does Evaluation of Explainable Artificial Intelligence Actually Tell Us? A Case for Compositional and Contextual Validation of XAI Building Blocks Kacper Sokol Julia E. Vogt 31 11 0 19 Mar 2024
Guidelines for Integrating Value Sensitive Design in Responsible AI Toolkits Malak Sadek Marios Constantinides Daniele Quercia C. Mougenot 35 14 0 29 Feb 2024
Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experience Q. V. Liao Hariharan Subramonyam Jennifer Wang Jennifer Wortman Vaughan HAI 22 58 0 21 Feb 2023
Selective Explanations: Leveraging Human Input to Align Explainable AI Vivian Lai Yiming Zhang Chacha Chen Q. V. Liao Chenhao Tan 16 43 0 23 Jan 2023
Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations Valerie Chen Q. V. Liao Jennifer Wortman Vaughan Gagan Bansal 38 104 0 18 Jan 2023
Perspectives on Incorporating Expert Feedback into Model Updates Valerie Chen Umang Bhatt Hoda Heidari Adrian Weller Ameet Talwalkar 30 11 0 13 May 2022
The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations Aparna Balagopalan Haoran Zhang Kimia Hamidieh Thomas Hartvigsen Frank Rudzicz Marzyeh Ghassemi 38 77 0 06 May 2022
DIME: Fine-grained Interpretations of Multimodal Models via Disentangled Local Explanations Yiwei Lyu Paul Pu Liang Zihao Deng Ruslan Salakhutdinov Louis-Philippe Morency 16 30 0 03 Mar 2022
Explainable Machine Learning for Public Policy: Use Cases, Gaps, and Research Directions Kasun Amarasinghe Kit Rodolfa Hemank Lamba Rayid Ghani ELM XAI 21 51 0 27 Oct 2020
Issues with post-hoc counterfactual explanations: a discussion Thibault Laugel Marie-Jeanne Lesot Christophe Marsala Marcin Detyniecki CML 104 44 0 11 Jun 2019
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 251 3,683 0 28 Feb 2017