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Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead

26 November 2018

Papers citing "Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead"

50 / 55 papers shown

Title
Efficient and Interpretable Neural Networks Using Complex Lehmer Transform M. Ataei Xiaogang Wang 87 0 0 28 Jan 2025
On Behalf of the Stakeholders: Trends in NLP Model Interpretability in the Era of LLMs Nitay Calderon Roi Reichart 127 16 0 27 Jul 2024
Black-Box Access is Insufficient for Rigorous AI Audits Stephen Casper Carson Ezell Charlotte Siegmann Noam Kolt Taylor Lynn Curtis ... Michael Gerovitch David Bau Max Tegmark David M. Krueger Dylan Hadfield-Menell AAML 143 94 0 25 Jan 2024
Faster Peace via Inclusivity: An Efficient Paradigm to Understand Populations in Conflict Zones Jordan Bilich Michael Varga Daanish Masood Andrew Konya 48 12 0 01 Nov 2023
A Survey on Explainability of Graph Neural Networks Jaykumar Kakkad Jaspal Jannu Kartik Sharma Charu C. Aggarwal Sourav Medya 60 28 0 02 Jun 2023
BELLA: Black box model Explanations by Local Linear Approximations N. Radulovic Albert Bifet Fabian M. Suchanek FAtt 116 1 0 18 May 2023
Less is More: The Influence of Pruning on the Explainability of CNNs David Weber F. Merkle Pascal Schöttle Stephan Schlögl Martin Nocker FAtt 196 1 0 17 Feb 2023
CI-GNN: A Granger Causality-Inspired Graph Neural Network for Interpretable Brain Network-Based Psychiatric Diagnosis Kaizhong Zheng Shujian Yu Badong Chen CML 125 33 0 04 Jan 2023
Automated Learning of Interpretable Models with Quantified Uncertainty Geoffrey F. Bomarito Patrick E. Leser N. Strauss K. Garbrecht J. D. Hochhalter 65 11 0 12 Apr 2022
Interpretation of Black Box NLP Models: A Survey Shivani Choudhary N. Chatterjee S. K. Saha FAtt 86 11 0 31 Mar 2022
Diagnosing AI Explanation Methods with Folk Concepts of Behavior Alon Jacovi Jasmijn Bastings Sebastian Gehrmann Yoav Goldberg Katja Filippova 105 17 0 27 Jan 2022
ProtGNN: Towards Self-Explaining Graph Neural Networks Zaixin Zhang Qi Liu Hao Wang Chengqiang Lu Chee-Kong Lee 71 130 0 02 Dec 2021
Interpreting and improving deep-learning models with reality checks Chandan Singh Wooseok Ha Bin Yu FAtt 84 3 0 16 Aug 2021
Attention, please! A survey of Neural Attention Models in Deep Learning Alana de Santana Correia Esther Luna Colombini HAI 121 194 0 31 Mar 2021
Local Interpretations for Explainable Natural Language Processing: A Survey Siwen Luo Hamish Ivison S. Han Josiah Poon MILM 120 50 0 20 Mar 2021
Towards an AI assistant for power grid operators Antoine Marot Alexandre Rozier Matthieu Dussartre Laure Crochepierre Benjamin Donnot AI4CE 59 10 0 03 Dec 2020
A Survey on the Explainability of Supervised Machine Learning Nadia Burkart Marco F. Huber FaML XAI 77 780 0 16 Nov 2020
Enforcing Interpretability and its Statistical Impacts: Trade-offs between Accuracy and Interpretability Gintare Karolina Dziugaite Shai Ben-David Daniel M. Roy FaML 37 40 0 26 Oct 2020
Generating End-to-End Adversarial Examples for Malware Classifiers Using Explainability Ishai Rosenberg Shai Meir J. Berrebi I. Gordon Guillaume Sicard Eli David AAML SILM 31 28 0 28 Sep 2020
Logic Programming and Machine Ethics Abeer Dyoub Stefania Costantini F. Lisi 41 11 0 22 Sep 2020
Region Comparison Network for Interpretable Few-shot Image Classification Z. Xue Lixin Duan Wen Li Lin Chen Jiebo Luo 61 16 0 08 Sep 2020
Conceptual Metaphors Impact Perceptions of Human-AI Collaboration Pranav Khadpe Ranjay Krishna Fei-Fei Li Jeffrey T. Hancock Michael S. Bernstein 89 109 0 05 Aug 2020
Explaining Deep Neural Networks using Unsupervised Clustering Yu-Han Liu Sercan O. Arik SSL AI4CE 73 11 0 15 Jul 2020
Improving Workflow Integration with xPath: Design and Evaluation of a Human-AI Diagnosis System in Pathology H. Gu Yuan Liang Yifan Xu Christopher Kazu Williams S. Magaki ... Wenzhong Yan X. R. Zhang Yang Li Mohammad Haeri Xiang Ánthony' Chen 91 31 0 23 Jun 2020
Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness? Alon Jacovi Yoav Goldberg XAI 136 600 0 07 Apr 2020
Causality-based Explanation of Classification Outcomes Leopoldo Bertossi Jordan Li Maximilian Schleich Dan Suciu Zografoula Vagena XAI CML FAtt 248 46 0 15 Mar 2020
A general framework for scientifically inspired explanations in AI David Tuckey A. Russo Krysia Broda 30 0 0 02 Mar 2020
Learning Global Transparent Models Consistent with Local Contrastive Explanations Tejaswini Pedapati Avinash Balakrishnan Karthikeyan Shanmugam Amit Dhurandhar FAtt 41 0 0 19 Feb 2020
Algorithmic Recourse: from Counterfactual Explanations to Interventions Amir-Hossein Karimi Bernhard Schölkopf Isabel Valera CML 76 346 0 14 Feb 2020
Exploring Benefits of Transfer Learning in Neural Machine Translation Tom Kocmi 57 17 0 06 Jan 2020
Dirichlet uncertainty wrappers for actionable algorithm accuracy accountability and auditability José Mena O. Pujol Jordi Vitrià 90 8 0 29 Dec 2019
AutoAIViz: Opening the Blackbox of Automated Artificial Intelligence with Conditional Parallel Coordinates D. Weidele Justin D. Weisz Eno Oduor Michael J. Muller Josh Andres Alexander G. Gray Dakuo Wang 94 54 0 13 Dec 2019
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI Alejandro Barredo Arrieta Natalia Díaz Rodríguez Javier Del Ser Adrien Bennetot Siham Tabik ... S. Gil-Lopez Daniel Molina Richard Benjamins Raja Chatila Francisco Herrera XAI 162 6,366 0 22 Oct 2019
FACE: Feasible and Actionable Counterfactual Explanations Rafael Poyiadzi Kacper Sokol Raúl Santos-Rodríguez T. D. Bie Peter A. Flach 84 371 0 20 Sep 2019
Towards a Rigorous Evaluation of XAI Methods on Time Series U. Schlegel Hiba Arnout Mennatallah El-Assady Daniela Oelke Daniel A. Keim XAI AI4TS 108 174 0 16 Sep 2019
Learning Fair Rule Lists Ulrich Aïvodji Julien Ferry Sébastien Gambs Marie-José Huguet Mohamed Siala FaML 55 11 0 09 Sep 2019
Visualizing Image Content to Explain Novel Image Discovery Jake H. Lee K. Wagstaff 29 3 0 14 Aug 2019
Attention is not not Explanation Sarah Wiegreffe Yuval Pinter XAI AAML FAtt 124 914 0 13 Aug 2019
Interpretable and Steerable Sequence Learning via Prototypes Yao Ming Panpan Xu Huamin Qu Liu Ren AI4TS 66 141 0 23 Jul 2019
The Dangers of Post-hoc Interpretability: Unjustified Counterfactual Explanations Thibault Laugel Marie-Jeanne Lesot Christophe Marsala X. Renard Marcin Detyniecki 85 197 0 22 Jul 2019
Forecasting remaining useful life: Interpretable deep learning approach via variational Bayesian inferences Mathias Kraus Stefan Feuerriegel 54 110 0 11 Jul 2019
Global and Local Interpretability for Cardiac MRI Classification J. Clough Ilkay Oksuz Esther Puyol-Antón B. Ruijsink A. King Julia A. Schnabel 92 60 0 14 Jun 2019
Understanding artificial intelligence ethics and safety David Leslie FaML AI4TS 74 363 0 11 Jun 2019
Issues with post-hoc counterfactual explanations: a discussion Thibault Laugel Marie-Jeanne Lesot Christophe Marsala Marcin Detyniecki CML 139 45 0 11 Jun 2019
Proposed Guidelines for the Responsible Use of Explainable Machine Learning Patrick Hall Navdeep Gill N. Schmidt SILM XAI FaML 77 29 0 08 Jun 2019
Model-Agnostic Counterfactual Explanations for Consequential Decisions Amir-Hossein Karimi Gilles Barthe Borja Balle Isabel Valera 107 323 0 27 May 2019
Hybrid Predictive Model: When an Interpretable Model Collaborates with a Black-box Model Tong Wang Qihang Lin 139 19 0 10 May 2019
The Scientific Method in the Science of Machine Learning Jessica Zosa Forde Michela Paganini 70 37 0 24 Apr 2019
Explaining Deep Classification of Time-Series Data with Learned Prototypes Alan H. Gee Diego Garcia-Olano Joydeep Ghosh D. Paydarfar AI4TS 103 67 0 18 Apr 2019
"Why did you do that?": Explaining black box models with Inductive Synthesis Görkem Paçaci David Johnson S. McKeever A. Hamfelt 35 6 0 17 Apr 2019