Human Factors in Model Interpretability: Industry Practices, Challenges, and Needs

23 April 2020

Papers citing "Human Factors in Model Interpretability: Industry Practices, Challenges, and Needs"

32 / 32 papers shown

Title
SPHERE: An Evaluation Card for Human-AI Systems Qianou Ma Dora Zhao Xinran Zhao Chenglei Si Chenyang Yang Ryan Louie Ehud Reiter Diyi Yang Tongshuang Wu ALM 103 1 0 24 Mar 2025
Rethinking Model Evaluation as Narrowing the Socio-Technical Gap Q. V. Liao Ziang Xiao ALM ELM 96 32 0 01 Jun 2023
Are Metrics Enough? Guidelines for Communicating and Visualizing Predictive Models to Subject Matter Experts Ashley Suh G. Appleby Erik W. Anderson Luca A. Finelli Remco Chang Dylan Cashman 95 8 0 11 May 2022
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective Satyapriya Krishna Tessa Han Alex Gu Steven Wu S. Jabbari Himabindu Lakkaraju 246 195 0 03 Feb 2022
Dissonance Between Human and Machine Understanding Zijian Zhang Jaspreet Singh U. Gadiraju Avishek Anand 109 74 0 18 Jan 2021
How do Data Science Workers Collaborate? Roles, Workflows, and Tools Amy X. Zhang Michael J. Muller Dakuo Wang FedML AI4CE 67 259 0 18 Jan 2020
Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI Dakuo Wang Justin D. Weisz Michael J. Muller Parikshit Ram Werner Geyer Casey Dugan Y. Tausczik Horst Samulowitz Alexander G. Gray 209 313 0 05 Sep 2019
Quantifying Interpretability and Trust in Machine Learning Systems Philipp Schmidt F. Biessmann 50 113 0 20 Jan 2019
Improving fairness in machine learning systems: What do industry practitioners need? Kenneth Holstein Jennifer Wortman Vaughan Hal Daumé Miroslav Dudík Hanna M. Wallach FaML HAI 245 760 0 13 Dec 2018
Automatically Explaining Machine Learning Prediction Results: A Demonstration on Type 2 Diabetes Risk Prediction G. Luo 30 85 0 06 Dec 2018
The Effect of Heterogeneous Data for Alzheimer's Disease Detection from Speech Aparna Balagopalan Jekaterina Novikova Frank Rudzicz Marzyeh Ghassemi 49 21 0 29 Nov 2018
Towards Explainable Deep Learning for Credit Lending: A Case Study C. Modarres Mark Ibrahim Melissa Louie John Paisley FaML 349 20 0 15 Nov 2018
Manifold: A Model-Agnostic Framework for Interpretation and Diagnosis of Machine Learning Models Jiawei Zhang Yang Wang Piero Molino Lezhi Li D. Ebert FAtt 48 204 0 01 Aug 2018
RuleMatrix: Visualizing and Understanding Classifiers with Rules Yao Ming Huamin Qu E. Bertini FAtt 62 215 0 17 Jul 2018
Explaining Explanations: An Overview of Interpretability of Machine Learning Leilani H. Gilpin David Bau Ben Z. Yuan Ayesha Bajwa Michael A. Specter Lalana Kagal XAI 93 1,857 0 31 May 2018
Human-in-the-Loop Interpretability Prior Isaac Lage A. Ross Been Kim S. Gershman Finale Doshi-Velez 77 121 0 29 May 2018
Manipulating and Measuring Model Interpretability Forough Poursabzi-Sangdeh D. Goldstein Jake M. Hofman Jennifer Wortman Vaughan Hanna M. Wallach 86 698 0 21 Feb 2018
A Survey Of Methods For Explaining Black Box Models Riccardo Guidotti A. Monreale Salvatore Ruggieri Franco Turini D. Pedreschi F. Giannotti XAI 124 3,961 0 06 Feb 2018
How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation Menaka Narayanan Emily Chen Jeffrey He Been Kim S. Gershman Finale Doshi-Velez FAtt XAI 104 242 0 02 Feb 2018
Direct-Manipulation Visualization of Deep Networks D. Smilkov Shan Carter D. Sculley F. Viégas Martin Wattenberg FAtt AI4CE 53 140 0 12 Aug 2017
Explanation in Artificial Intelligence: Insights from the Social Sciences Tim Miller XAI 242 4,265 0 22 Jun 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 1.1K 21,906 0 22 May 2017
ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models Minsuk Kahng Pierre Yves Andrews Aditya Kalro Duen Horng Chau HAI 69 324 0 06 Apr 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 399 3,798 0 28 Feb 2017
Improving Human-Machine Cooperative Visual Search With Soft Highlighting R. T. Kneusel Michael C. Mozer 46 26 0 24 Dec 2016
Detecting Dependencies in Sparse, Multivariate Databases Using Probabilistic Programming and Non-parametric Bayes Feras A. Saad Vikash K. Mansinghka 30 14 0 05 Nov 2016
The Mythos of Model Interpretability Zachary Chase Lipton FaML 180 3,701 0 10 Jun 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 16,976 0 16 Feb 2016
BayesDB: A probabilistic programming system for querying the probable implications of data Vikash K. Mansinghka R. Tibbetts Jay Baxter Pat Shafto Baxter S. Eaves 46 38 0 15 Dec 2015
Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model Benjamin Letham Cynthia Rudin Tyler H. McCormick D. Madigan FAtt 65 743 0 05 Nov 2015
Monotonic Calibrated Interpolated Look-Up Tables Maya R. Gupta Andrew Cotter Jan Pfeifer Konstantin Voevodski K. Canini Alexander Mangylov Wojtek Moczydlowski A. V. Esbroeck 189 128 0 23 May 2015
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Karen Simonyan Andrea Vedaldi Andrew Zisserman FAtt 312 7,295 0 20 Dec 2013