"How do I fool you?": Manipulating User Trust via Misleading Black Box Explanations

15 November 2019

Himabindu Lakkaraju

Papers citing ""How do I fool you?": Manipulating User Trust via Misleading Black Box Explanations"

50 / 57 papers shown

Title
What Do People Want to Know About Artificial Intelligence (AI)? The Importance of Answering End-User Questions to Explain Autonomous Vehicle (AV) Decisions Somayeh Molaei Lionel P. Robert Nikola Banovic 31 0 0 09 May 2025
Towards Responsible and Trustworthy Educational Data Mining: Comparing Symbolic, Sub-Symbolic, and Neural-Symbolic AI Methods Danial Hooshyar Eve Kikas Yeongwook Yang Gustav Šír Raija Hamalainen T. Karkkainen Roger Azevedo 64 1 0 01 Apr 2025
Don't be Fooled: The Misinformation Effect of Explanations in Human-AI Collaboration Philipp Spitzer Joshua Holstein Katelyn Morrison Kenneth Holstein Gerhard Satzger Niklas Kühl 50 3 0 19 Sep 2024
Algebraic Adversarial Attacks on Integrated Gradients Lachlan Simpson Federico Costanza Kyle Millar A. Cheng Cheng-Chew Lim Hong-Gunn Chew SILM AAML 69 2 0 23 Jul 2024
Efficient Exploration of the Rashomon Set of Rule Set Models Martino Ciaperoni Han Xiao Aristides Gionis 36 3 0 05 Jun 2024
Mapping the Potential of Explainable AI for Fairness Along the AI Lifecycle Luca Deck Astrid Schomacker Timo Speith Jakob Schöffer Lena Kästner Niklas Kühl 48 4 0 29 Apr 2024
Mindful Explanations: Prevalence and Impact of Mind Attribution in XAI Research Susanne Hindennach Lei Shi Filip Miletić Andreas Bulling 22 4 0 19 Dec 2023
Trust, distrust, and appropriate reliance in (X)AI: a survey of empirical evaluation of user trust Roel W. Visser Tobias M. Peters Ingrid Scharlau Barbara Hammer 29 5 0 04 Dec 2023
On the Relationship Between Interpretability and Explainability in Machine Learning Benjamin Leblanc Pascal Germain FaML 36 0 0 20 Nov 2023
Predictability and Comprehensibility in Post-Hoc XAI Methods: A User-Centered Analysis Anahid N. Jalali Bernhard Haslhofer Simone Kriglstein Andreas Rauber FAtt 39 4 0 21 Sep 2023
Discriminative Feature Attributions: Bridging Post Hoc Explainability and Inherent Interpretability Usha Bhalla Suraj Srinivas Himabindu Lakkaraju FAtt CML 39 6 0 27 Jul 2023
Impact Of Explainable AI On Cognitive Load: Insights From An Empirical Study L. Herm 26 22 0 18 Apr 2023
A Systematic Literature Review of User Trust in AI-Enabled Systems: An HCI Perspective T. A. Bach Amna Khan Harry P. Hallock Gabriel Beltrao Sonia C. Sousa 27 100 0 18 Apr 2023
Robust Explanation Constraints for Neural Networks Matthew Wicker Juyeon Heo Luca Costabello Adrian Weller FAtt 34 18 0 16 Dec 2022
Learning to Select Prototypical Parts for Interpretable Sequential Data Modeling Yifei Zhang Nengneng Gao Cunqing Ma 25 6 0 07 Dec 2022
Towards More Robust Interpretation via Local Gradient Alignment Sunghwan Joo Seokhyeon Jeong Juyeon Heo Adrian Weller Taesup Moon FAtt 38 5 0 29 Nov 2022
On the Robustness of Explanations of Deep Neural Network Models: A Survey Amlan Jyoti Karthik Balaji Ganesh Manoj Gayala Nandita Lakshmi Tunuguntla Sandesh Kamath V. Balasubramanian XAI FAtt AAML 37 4 0 09 Nov 2022
Logic-Based Explainability in Machine Learning Sasha Rubin LRM XAI 52 39 0 24 Oct 2022
The Influence of Explainable Artificial Intelligence: Nudging Behaviour or Boosting Capability? Matija Franklin TDI 23 1 0 05 Oct 2022
Explanations, Fairness, and Appropriate Reliance in Human-AI Decision-Making Jakob Schoeffer Maria De-Arteaga Niklas Kuehl FaML 55 46 0 23 Sep 2022
Inferring Sensitive Attributes from Model Explanations Vasisht Duddu A. Boutet MIACV SILM 24 16 0 21 Aug 2022
Causality-Inspired Taxonomy for Explainable Artificial Intelligence Pedro C. Neto Tiago B. Gonccalves João Ribeiro Pinto W. Silva Ana F. Sequeira Arun Ross Jaime S. Cardoso XAI 43 12 0 19 Aug 2022
Algorithmic Assistance with Recommendation-Dependent Preferences Bryce Mclaughlin Jann Spiess 47 9 0 16 Aug 2022
On Computing Relevant Features for Explaining NBCs Yacine Izza Sasha Rubin 38 5 0 11 Jul 2022
A Human-Centric Take on Model Monitoring Murtuza N. Shergadwala Himabindu Lakkaraju K. Kenthapadi 45 9 0 06 Jun 2022
Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations Jessica Dai Sohini Upadhyay Ulrich Aïvodji Stephen H. Bach Himabindu Lakkaraju 53 57 0 15 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 45 78 0 06 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 194 186 0 03 Feb 2022
Debiased-CAM to mitigate systematic error with faithful visual explanations of machine learning Wencan Zhang Mariella Dimiccoli Brian Y. Lim FAtt 30 1 0 30 Jan 2022
Global explainability in aligned image modalities Justin Engelmann Amos Storkey Miguel O. Bernabeu FAtt 35 4 0 17 Dec 2021
On the Fairness of Machine-Assisted Human Decisions Talia B. Gillis Bryce Mclaughlin Jann Spiess FaML 29 16 0 28 Oct 2021
Unpacking the Black Box: Regulating Algorithmic Decisions Laura Blattner Scott Nelson Jann Spiess MLAU FaML 28 19 0 05 Oct 2021
Toward a Unified Framework for Debugging Concept-based Models A. Bontempelli Fausto Giunchiglia Andrea Passerini Stefano Teso 20 4 0 23 Sep 2021
InfoGram and Admissible Machine Learning S. Mukhopadhyay FaML 24 8 0 17 Aug 2021
GCExplainer: Human-in-the-Loop Concept-based Explanations for Graph Neural Networks Lucie Charlotte Magister Dmitry Kazhdan Vikash Singh Pietro Lio 35 48 0 25 Jul 2021
On the Veracity of Local, Model-agnostic Explanations in Audio Classification: Targeted Investigations with Adversarial Examples Verena Praher Katharina Prinz A. Flexer Gerhard Widmer AAML FAtt 19 9 0 19 Jul 2021
What will it take to generate fairness-preserving explanations? Jessica Dai Sohini Upadhyay Stephen H. Bach Himabindu Lakkaraju FAtt FaML 21 14 0 24 Jun 2021
Synthetic Benchmarks for Scientific Research in Explainable Machine Learning Yang Liu Sujay Khandagale Colin White Willie Neiswanger 37 65 0 23 Jun 2021
Characterizing the risk of fairwashing Ulrich Aïvodji Hiromi Arai Sébastien Gambs Satoshi Hara 23 27 0 14 Jun 2021
On Efficiently Explaining Graph-Based Classifiers Xuanxiang Huang Yacine Izza Alexey Ignatiev Sasha Rubin FAtt 38 37 0 02 Jun 2021
Information-theoretic Evolution of Model Agnostic Global Explanations Sukriti Verma Nikaash Puri Piyush B. Gupta Balaji Krishnamurthy FAtt 29 0 0 14 May 2021
SAT-Based Rigorous Explanations for Decision Lists Alexey Ignatiev Sasha Rubin XAI 29 44 0 14 May 2021
Local Explanations via Necessity and Sufficiency: Unifying Theory and Practice David S. Watson Limor Gultchin Ankur Taly Luciano Floridi 22 63 0 27 Mar 2021
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges Cynthia Rudin Chaofan Chen Zhi Chen Haiyang Huang Lesia Semenova Chudi Zhong FaML AI4CE LRM 59 655 0 20 Mar 2021
Detecting Spurious Correlations with Sanity Tests for Artificial Intelligence Guided Radiology Systems U. Mahmood Robik Shrestha D. Bates L. Mannelli G. Corrias Y. Erdi Christopher Kanan 18 16 0 04 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 29 146 0 26 Feb 2021
Connecting Interpretability and Robustness in Decision Trees through Separation Michal Moshkovitz Yao-Yuan Yang Kamalika Chaudhuri 33 22 0 14 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
Debiased-CAM to mitigate image perturbations with faithful visual explanations of machine learning Wencan Zhang Mariella Dimiccoli Brian Y. Lim FAtt 34 18 0 10 Dec 2020
Robust and Stable Black Box Explanations Himabindu Lakkaraju Nino Arsov Osbert Bastani AAML FAtt 24 84 0 12 Nov 2020