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"

31 / 31 papers shown

Title
On Behalf of the Stakeholders: Trends in NLP Model Interpretability in the Era of LLMs Nitay Calderon Roi Reichart 40 10 0 27 Jul 2024
Learning accurate and interpretable tree-based models Maria-Florina Balcan Dravyansh Sharma 42 7 0 24 May 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 34 78 0 25 Jan 2024
BELLA: Black box model Explanations by Local Linear Approximations N. Radulovic Albert Bifet Fabian M. Suchanek FAtt 37 1 0 18 May 2023
Finding the Needle in a Haystack: Unsupervised Rationale Extraction from Long Text Classifiers Kamil Bujel Andrew Caines H. Yannakoudakis Marek Rei AI4TS 19 1 0 14 Mar 2023
Less is More: The Influence of Pruning on the Explainability of CNNs David Weber F. Merkle Pascal Schöttle Stephan Schlögl Martin Nocker FAtt 34 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 33 31 0 04 Jan 2023
"Explanation" is Not a Technical Term: The Problem of Ambiguity in XAI Leilani H. Gilpin Andrew R. Paley M. A. Alam Sarah Spurlock Kristian J. Hammond XAI 26 6 0 27 Jun 2022
Interpretation of Black Box NLP Models: A Survey Shivani Choudhary N. Chatterjee S. K. Saha FAtt 34 10 0 31 Mar 2022
Analogies and Feature Attributions for Model Agnostic Explanation of Similarity Learners K. Ramamurthy Amit Dhurandhar Dennis L. Wei Zaid Bin Tariq FAtt 38 3 0 02 Feb 2022
Diagnosing AI Explanation Methods with Folk Concepts of Behavior Alon Jacovi Jasmijn Bastings Sebastian Gehrmann Yoav Goldberg Katja Filippova 36 15 0 27 Jan 2022
Foundations of Symbolic Languages for Model Interpretability Marcelo Arenas Daniel Baez Pablo Barceló Jorge A. Pérez Bernardo Subercaseaux ReLM LRM 21 24 0 05 Oct 2021
Combining Transformers with Natural Language Explanations Federico Ruggeri Marco Lippi Paolo Torroni 25 1 0 02 Sep 2021
Local Interpretations for Explainable Natural Language Processing: A Survey Siwen Luo Hamish Ivison S. Han Josiah Poon MILM 33 48 0 20 Mar 2021
Enforcing Interpretability and its Statistical Impacts: Trade-offs between Accuracy and Interpretability Gintare Karolina Dziugaite Shai Ben-David Daniel M. Roy FaML 17 38 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 11 25 0 28 Sep 2020
Conceptual Metaphors Impact Perceptions of Human-AI Collaboration Pranav Khadpe Ranjay Krishna Fei-Fei Li Jeffrey T. Hancock Michael S. Bernstein 25 105 0 05 Aug 2020
Learning Global Transparent Models Consistent with Local Contrastive Explanations Tejaswini Pedapati Avinash Balakrishnan Karthikeyan Shanmugam Amit Dhurandhar FAtt 22 0 0 19 Feb 2020
Algorithmic Recourse: from Counterfactual Explanations to Interventions Amir-Hossein Karimi Bernhard Schölkopf Isabel Valera CML 24 337 0 14 Feb 2020
Exploring Benefits of Transfer Learning in Neural Machine Translation Tom Kocmi 19 17 0 06 Jan 2020
Dirichlet uncertainty wrappers for actionable algorithm accuracy accountability and auditability José Mena O. Pujol Jordi Vitrià 21 8 0 29 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 37 6,110 0 22 Oct 2019
Visualizing Image Content to Explain Novel Image Discovery Jake H. Lee K. Wagstaff 18 3 0 14 Aug 2019
Model-Agnostic Counterfactual Explanations for Consequential Decisions Amir-Hossein Karimi Gilles Barthe Borja Balle Isabel Valera 44 317 0 27 May 2019
Hybrid Predictive Model: When an Interpretable Model Collaborates with a Black-box Model Tong Wang Qihang Lin 38 19 0 10 May 2019
The Scientific Method in the Science of Machine Learning Jessica Zosa Forde Michela Paganini 24 35 0 24 Apr 2019
VINE: Visualizing Statistical Interactions in Black Box Models M. Britton FAtt 17 21 0 01 Apr 2019
Interpreting Neural Networks Using Flip Points Roozbeh Yousefzadeh D. O’Leary AAML FAtt 22 17 0 21 Mar 2019
SAFE ML: Surrogate Assisted Feature Extraction for Model Learning Alicja Gosiewska A. Gacek Piotr Lubon P. Biecek 13 5 0 28 Feb 2019
Fairwashing: the risk of rationalization Ulrich Aïvodji Hiromi Arai O. Fortineau Sébastien Gambs Satoshi Hara Alain Tapp FaML 19 142 0 28 Jan 2019
Interpretable machine learning: definitions, methods, and applications W. James Murdoch Chandan Singh Karl Kumbier R. Abbasi-Asl Bin-Xia Yu XAI HAI 47 1,416 0 14 Jan 2019