Explanation in Artificial Intelligence: Insights from the Social Sciences

22 June 2017

Papers citing "Explanation in Artificial Intelligence: Insights from the Social Sciences"

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Title
Making Things Explainable vs Explaining: Requirements and Challenges under the GDPR Francesco Sovrano F. Vitali M. Palmirani 35 10 0 02 Oct 2021
Critical Empirical Study on Black-box Explanations in AI Jean-Marie John-Mathews 14 6 0 29 Sep 2021
Trustworthy AI and Robotics and the Implications for the AEC Industry: A Systematic Literature Review and Future Potentials Newsha Emaminejad Reza Akhavian 33 49 0 27 Sep 2021
A User-Centred Framework for Explainable Artificial Intelligence in Human-Robot Interaction Marco Matarese F. Rea A. Sciutti 32 13 0 27 Sep 2021
Explainability Pitfalls: Beyond Dark Patterns in Explainable AI Upol Ehsan Mark O. Riedl XAI SILM 59 58 0 26 Sep 2021
Explanation Strategies as an Empirical-Analytical Lens for Socio-Technical Contextualization of Machine Learning Interpretability J. Benjamin C. Kinkeldey Claudia Muller-Birn Tim Korjakow Eva-Maria Herbst 83 8 0 24 Sep 2021
Interpretable Directed Diversity: Leveraging Model Explanations for Iterative Crowd Ideation Yunlong Wang Priyadarshini Venkatesh Brian Y. Lim 38 20 0 21 Sep 2021
Counterfactual Instances Explain Little Adam White Artur Garcez CML 34 5 0 20 Sep 2021
Some Critical and Ethical Perspectives on the Empirical Turn of AI Interpretability Jean-Marie John-Mathews 52 34 0 20 Sep 2021
Interpretable Local Tree Surrogate Policies John Mern Sidhart Krishnan Anil Yildiz Kyle Hatch Mykel J. Kochenderfer OffRL 23 0 0 16 Sep 2021
Detection Accuracy for Evaluating Compositional Explanations of Units Sayo M. Makinwa Biagio La Rosa Roberto Capobianco FAtt CoGe 49 1 0 16 Sep 2021
CounterNet: End-to-End Training of Prediction Aware Counterfactual Explanations Hangzhi Guo T. Nguyen A. Yadav OffRL 24 14 0 15 Sep 2021
AdViCE: Aggregated Visual Counterfactual Explanations for Machine Learning Model Validation Oscar Gomez Steffen Holter Jun Yuan E. Bertini AAML CML HAI 24 21 0 12 Sep 2021
An Objective Metric for Explainable AI: How and Why to Estimate the Degree of Explainability Francesco Sovrano F. Vitali 45 30 0 11 Sep 2021
Modelling GDPR-Compliant Explanations for Trustworthy AI Francesco Sovrano F. Vitali M. Palmirani 37 16 0 09 Sep 2021
Model Explanations via the Axiomatic Causal Lens Gagan Biradar Vignesh Viswanathan Yair Zick XAI CML 25 1 0 08 Sep 2021
ExCode-Mixed: Explainable Approaches towards Sentiment Analysis on Code-Mixed Data using BERT models Aman Priyanshu Aleti Vardhan Sudarshan Sivakumar Supriti Vijay Nipuna Chhabra 24 0 0 07 Sep 2021
CX-ToM: Counterfactual Explanations with Theory-of-Mind for Enhancing Human Trust in Image Recognition Models Arjun Reddy Akula Keze Wang Changsong Liu Sari Saba-Sadiya Hongjing Lu S. Todorovic J. Chai Song-Chun Zhu 31 47 0 03 Sep 2021
Improving HRI through robot architecture transparency L. Hindemith Anna-Lisa Vollmer Christiane B. Wiebel-Herboth Britta Wrede 19 1 0 26 Aug 2021
VAE-CE: Visual Contrastive Explanation using Disentangled VAEs Y. Poels Vlado Menkovski CoGe DRL 37 3 0 20 Aug 2021
Explainable Reinforcement Learning for Broad-XAI: A Conceptual Framework and Survey Richard Dazeley Peter Vamplew Francisco Cruz 32 61 0 20 Aug 2021
Towards Visual Explainable Active Learning for Zero-Shot Classification Shichao Jia Zeyu Li Nuo Chen Jiawan Zhang VLM 30 23 0 15 Aug 2021
Seven challenges for harmonizing explainability requirements Jiahao Chen Victor Storchan 36 8 0 11 Aug 2021
Trading Complexity for Sparsity in Random Forest Explanations Gilles Audemard S. Bellart Louenas Bounia F. Koriche Jean-Marie Lagniez Pierre Marquis 27 39 0 11 Aug 2021
On the Explanatory Power of Decision Trees Gilles Audemard S. Bellart Louenas Bounia F. Koriche Jean-Marie Lagniez Pierre Marquis FAtt 22 11 0 11 Aug 2021
Logic Explained Networks Gabriele Ciravegna Pietro Barbiero Francesco Giannini Marco Gori Pietro Lio Marco Maggini S. Melacci 42 69 0 11 Aug 2021
Post-hoc Interpretability for Neural NLP: A Survey Andreas Madsen Siva Reddy A. Chandar XAI 32 225 0 10 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 24 37 0 04 Aug 2021
Accelerating the Learning of TAMER with Counterfactual Explanations Jakob Karalus F. Lindner OffRL 29 4 0 03 Aug 2021
Discovering User-Interpretable Capabilities of Black-Box Planning Agents Pulkit Verma Shashank Rao Marpally Siddharth Srivastava ELM LLMAG 16 20 0 28 Jul 2021
The Who in XAI: How AI Background Shapes Perceptions of AI Explanations Upol Ehsan Samir Passi Q. V. Liao Larry Chan I-Hsiang Lee Michael J. Muller Mark O. Riedl 34 86 0 28 Jul 2021
Resisting Out-of-Distribution Data Problem in Perturbation of XAI Luyu Qiu Yi Yang Caleb Chen Cao Jing Liu Yueyuan Zheng H. Ngai J. H. Hsiao Lei Chen 19 18 0 27 Jul 2021
Improving Visualization Interpretation Using Counterfactuals Smiti Kaul D. Borland Nan Cao David Gotz CML 16 17 0 21 Jul 2021
Uncertainty Estimation and Out-of-Distribution Detection for Counterfactual Explanations: Pitfalls and Solutions Eoin Delaney Derek Greene Mark T. Keane 38 24 0 20 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 46 21 0 20 Jul 2021
Roadmap of Designing Cognitive Metrics for Explainable Artificial Intelligence (XAI) J. H. Hsiao H. Ngai Luyu Qiu Yi Yang Caleb Chen Cao XAI 33 27 0 20 Jul 2021
Using automated decision-making (ADM) to allocate Covid-19 vaccinations? Exploring the roles of trust and social group preference on the legitimacy of ADM vs. human decision-making Marco Lünich Kimon Kieslich 40 13 0 19 Jul 2021
Model Uncertainty and Correctability for Directed Graphical Models Panagiota Birmpa Jinchao Feng Markos A. Katsoulakis Luc Rey-Bellet 29 0 0 17 Jul 2021
Do Humans Trust Advice More if it Comes from AI? An Analysis of Human-AI Interactions Kailas Vodrahalli Roxana Daneshjou Tobias Gerstenberg James Zou 55 68 0 14 Jul 2021
Trustworthy AI: A Computational Perspective Haochen Liu Yiqi Wang Wenqi Fan Xiaorui Liu Yaxin Li Shaili Jain Yunhao Liu Anil K. Jain Jiliang Tang FaML 104 198 0 12 Jul 2021
The Role of Social Movements, Coalitions, and Workers in Resisting Harmful Artificial Intelligence and Contributing to the Development of Responsible AI Susan von Struensee 16 3 0 11 Jul 2021
A Framework and Benchmarking Study for Counterfactual Generating Methods on Tabular Data Raphael Mazzine David Martens 40 33 0 09 Jul 2021
Contrastive Explanations for Argumentation-Based Conclusions A. Borg Floris Bex 16 7 0 07 Jul 2021
Levels of explainable artificial intelligence for human-aligned conversational explanations Richard Dazeley Peter Vamplew Cameron Foale Charlotte Young Sunil Aryal F. Cruz 32 90 0 07 Jul 2021
Understanding Consumer Preferences for Explanations Generated by XAI Algorithms Yanou Ramon T. Vermeire Olivier Toubia David Martens Theodoros Evgeniou 39 10 0 06 Jul 2021
Improving a neural network model by explanation-guided training for glioma classification based on MRI data Frantisek Sefcik Wanda Benesova 27 12 0 05 Jul 2021
ARM-Net: Adaptive Relation Modeling Network for Structured Data Shaofeng Cai Kaiping Zheng Gang Chen H. V. Jagadish Beng Chin Ooi Meihui Zhang 43 50 0 05 Jul 2021
An Explainable AI System for the Diagnosis of High Dimensional Biomedical Data A. Ultsch J. Hoffmann M. Röhnert M. Bonin U. Oelschlägel C. Brendel M. Thrun 11 7 0 05 Jul 2021
Efficient Explanations for Knowledge Compilation Languages Xuanxiang Huang Yacine Izza Alexey Ignatiev Martin C. Cooper Nicholas M. Asher Sasha Rubin 25 15 0 04 Jul 2021
Exploring the Efficacy of Automatically Generated Counterfactuals for Sentiment Analysis Linyi Yang Jiazheng Li Padraig Cunningham Yue Zhang Barry Smyth Ruihai Dong 27 47 0 29 Jun 2021