The What-If Tool: Interactive Probing of Machine Learning Models

9 July 2019

Papers citing "The What-If Tool: Interactive Probing of Machine Learning Models"

50 / 230 papers shown

Title
Towards Explainable Artificial Intelligence in Banking and Financial Services Ambreen Hanif 6 9 0 14 Dec 2021
A Framework for Fairness: A Systematic Review of Existing Fair AI Solutions Brianna Richardson J. Gilbert FaML 10 36 0 10 Dec 2021
MCCE: Monte Carlo sampling of realistic counterfactual explanations Annabelle Redelmeier Martin Jullum K. Aas Anders Løland BDL 18 11 0 18 Nov 2021
Interactive Analysis of CNN Robustness Stefan Sietzen Mathias Lechner Judy Borowski Ramin Hasani Manuela Waldner AAML 24 10 0 14 Oct 2021
A Sociotechnical View of Algorithmic Fairness Mateusz Dolata Stefan Feuerriegel Gerhard Schwabe FaML 30 92 0 27 Sep 2021
Augmenting Decision Making via Interactive What-If Analysis Sneha Gathani Madelon Hulsebos James Gale P. Haas cCaugatay Demiralp 14 8 0 13 Sep 2021
AdViCE: Aggregated Visual Counterfactual Explanations for Machine Learning Model Validation Oscar Gomez Steffen Holter Jun Yuan E. Bertini AAML CML HAI 16 21 0 12 Sep 2021
Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability in the Cloud Michaela Hardt Xiaoguang Chen Xiaoyi Cheng Michele Donini J. Gelman ... Muhammad Bilal Zafar Sanjiv Ranjan Das Kevin Haas Tyler Hill K. Kenthapadi ELM FaML 21 42 0 07 Sep 2021
Contrastive Identification of Covariate Shift in Image Data Matthew Lyle Olson Thu Nguyen Gaurav Dixit Neale Ratzlaff Weng-Keen Wong Minsuk Kahng OOD 12 8 0 18 Aug 2021
VBridge: Connecting the Dots Between Features and Data to Explain Healthcare Models Furui Cheng Dongyu Liu F. Du Yanna Lin Alexandra Zytek Haomin Li Huamin Qu K. Veeramachaneni 13 37 0 04 Aug 2021
Improving Visualization Interpretation Using Counterfactuals Smiti Kaul D. Borland Nan Cao David Gotz CML 8 17 0 21 Jul 2021
Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutions Eike Petersen Yannik Potdevin Esfandiar Mohammadi Stephan Zidowitz Sabrina Breyer ... Sandra Henn Ludwig Pechmann M. Leucker P. Rostalski Christian Herzog FaML AILaw OOD 27 21 0 20 Jul 2021
Shared Interest: Measuring Human-AI Alignment to Identify Recurring Patterns in Model Behavior Angie Boggust Benjamin Hoover Arvindmani Satyanarayan Hendrik Strobelt 19 50 0 20 Jul 2021
M2Lens: Visualizing and Explaining Multimodal Models for Sentiment Analysis Xingbo Wang Jianben He Zhihua Jin Muqiao Yang Yong Wang Huamin Qu 6 75 0 17 Jul 2021
The Spotlight: A General Method for Discovering Systematic Errors in Deep Learning Models G. dÉon Jason dÉon J. R. Wright Kevin Leyton-Brown 20 74 0 01 Jul 2021
Productivity, Portability, Performance: Data-Centric Python Yiheng Wang Yao Zhang Yanzhang Wang Yan Wan Jiao Wang Zhongyuan Wu Yuhao Yang Bowen She 49 94 0 01 Jul 2021
Explanatory Pluralism in Explainable AI Yiheng Yao XAI 12 4 0 26 Jun 2021
Meaningfully Debugging Model Mistakes using Conceptual Counterfactual Explanations Abubakar Abid Mert Yuksekgonul James Y. Zou CML 26 64 0 24 Jun 2021
Rational Shapley Values David S. Watson 15 19 0 18 Jun 2021
FairCanary: Rapid Continuous Explainable Fairness Avijit Ghosh Aalok Shanbhag Christo Wilson 11 20 0 13 Jun 2021
FedNLP: An interpretable NLP System to Decode Federal Reserve Communications Jean Lee Hoyoul Luis Youn Nicholas Stevens Josiah Poon S. Han 11 10 0 11 Jun 2021
Explainable Machine Learning with Prior Knowledge: An Overview Katharina Beckh Sebastian Müller Matthias Jakobs Vanessa Toborek Hanxiao Tan Raphael Fischer Pascal Welke Sebastian Houben Laura von Rueden XAI 17 28 0 21 May 2021
Cause and Effect: Hierarchical Concept-based Explanation of Neural Networks Mohammad Nokhbeh Zaeem Majid Komeili CML 8 9 0 14 May 2021
When Fair Ranking Meets Uncertain Inference Avijit Ghosh Ritam Dutt Christo Wilson 25 44 0 05 May 2021
TrustyAI Explainability Toolkit Rob Geada Tommaso Teofili Rui Vieira Rebecca Whitworth Daniele Zonca 16 2 0 26 Apr 2021
NICE: An Algorithm for Nearest Instance Counterfactual Explanations Dieter Brughmans Pieter Leyman David Martens 25 63 0 15 Apr 2021
Does My Representation Capture X? Probe-Ably Deborah Ferreira Julia Rozanova Mokanarangan Thayaparan Marco Valentino André Freitas 11 11 0 12 Apr 2021
Why? Why not? When? Visual Explanations of Agent Behavior in Reinforcement Learning Aditi Mishra Utkarsh Soni Jinbin Huang Chris Bryan OffRL 14 23 0 06 Apr 2021
Coalitional strategies for efficient individual prediction explanation Gabriel Ferrettini Elodie Escriva Julien Aligon Jean-Baptiste Excoffier C. Soulé-Dupuy 17 17 0 01 Apr 2021
Local Explanations via Necessity and Sufficiency: Unifying Theory and Practice David S. Watson Limor Gultchin Ankur Taly Luciano Floridi 12 62 0 27 Mar 2021
Fairness On The Ground: Applying Algorithmic Fairness Approaches to Production Systems Chloé Bakalar Renata Barreto Stevie Bergman Miranda Bogen Bobbie Chern ... J. Simons Jonathan Tannen Edmund Tong Kate Vredenburgh Jiejing Zhao FaML 11 26 0 10 Mar 2021
Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applications Yu-Liang Chou Catarina Moreira P. Bruza Chun Ouyang Joaquim A. Jorge CML 42 176 0 07 Mar 2021
WordBias: An Interactive Visual Tool for Discovering Intersectional Biases Encoded in Word Embeddings Bhavya Ghai Md. Naimul Hoque Klaus Mueller 15 26 0 05 Mar 2021
Counterfactual Explanations for Oblique Decision Trees: Exact, Efficient Algorithms Miguel Á. Carreira-Perpiñán Suryabhan Singh Hada CML AAML 11 33 0 01 Mar 2021
If Only We Had Better Counterfactual Explanations: Five Key Deficits to Rectify in the Evaluation of Counterfactual XAI Techniques Mark T. Keane Eoin M. Kenny Eoin Delaney Barry Smyth CML 11 146 0 26 Feb 2021
Intuitively Assessing ML Model Reliability through Example-Based Explanations and Editing Model Inputs Harini Suresh Kathleen M. Lewis John Guttag Arvind Satyanarayan FAtt 32 25 0 17 Feb 2021
RECAST: Enabling User Recourse and Interpretability of Toxicity Detection Models with Interactive Visualization Austin P. Wright Omar Shaikh Haekyu Park Will Epperson Muhammed Ahmed Stephane Pinel Duen Horng Chau Diyi Yang 17 21 0 08 Feb 2021
BeFair: Addressing Fairness in the Banking Sector Alessandro Castelnovo Riccardo Crupi Giulia Del Gamba Greta Greco A. Naseer D. Regoli Beatriz San Miguel González FaML 10 16 0 03 Feb 2021
Evaluating the Interpretability of Generative Models by Interactive Reconstruction A. Ross Nina Chen Elisa Zhao Hang Elena L. Glassman Finale Doshi-Velez 103 49 0 02 Feb 2021
Soliciting Stakeholders' Fairness Notions in Child Maltreatment Predictive Systems H. Cheng Logan Stapleton Ruiqi Wang Paige E Bullock Alexandra Chouldechova Zhiwei Steven Wu Haiyi Zhu FaML 10 66 0 01 Feb 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
Understanding the Effect of Out-of-distribution Examples and Interactive Explanations on Human-AI Decision Making Han Liu Vivian Lai Chenhao Tan 19 116 0 13 Jan 2021
Robustness Gym: Unifying the NLP Evaluation Landscape Karan Goel Nazneen Rajani Jesse Vig Samson Tan Jason M. Wu Stephan Zheng Caiming Xiong Mohit Bansal Christopher Ré AAML OffRL OOD 146 136 0 13 Jan 2021
GeCo: Quality Counterfactual Explanations in Real Time Maximilian Schleich Zixuan Geng Yihong Zhang D. Suciu 38 61 0 05 Jan 2021
Outcome-Explorer: A Causality Guided Interactive Visual Interface for Interpretable Algorithmic Decision Making Md. Naimul Hoque Klaus Mueller CML 48 30 0 03 Jan 2021
dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Python Hubert Baniecki Wojciech Kretowicz Piotr Piątyszek J. Wiśniewski P. Biecek FaML 21 95 0 28 Dec 2020
A Statistical Test for Probabilistic Fairness Bahar Taşkesen Jose H. Blanchet Daniel Kuhn Viet Anh Nguyen FaML 6 37 0 09 Dec 2020
GNNLens: A Visual Analytics Approach for Prediction Error Diagnosis of Graph Neural Networks Zhihua Jin Yong Wang Qianwen Wang Yao Ming Tengfei Ma Huamin Qu HAI 10 31 0 22 Nov 2020
TBSSvis: Visual Analytics for Temporal Blind Source Separation Nikolaus Piccolotto M. Bögl T. Gschwandtner C. Muehlmann K. Nordhausen Peter Filzmoser Silvia Miksch AI4TS 16 8 0 19 Nov 2020
HypperSteer: Hypothetical Steering and Data Perturbation in Sequence Prediction with Deep Learning Chuan-Chi Wang K. Ma OOD LLMSV 14 3 0 04 Nov 2020