Fetching the paper…
Reading the bibliography…
A surge of interest in explainable AI (XAI) has led to a vast collection of algorithmic work on the topic.
The bayesian case model: A generative approach for case-based reasoning and prototype classification. In Advances in Neural Information Processing Systems
Been Kim, Cynthia Rudin, and Julie A Shah. 2014 · 1960
Earlier work this paper cites.
The epistemology of a rule-based expert system—a framework for explanation
William J Clancey. 1983 · 1983
Earlier work this paper cites.
XPLAIN: A system for creating and explaining expert consulting programs
William R Swartout. 1983 · 1983
Earlier work this paper cites.
On making expert systems more like experts
William R Swartout and Stephen W Smoliar. 1987 · 1987
Earlier work this paper cites.
Explaining control strategies in problem solving
Bruce Chandrasekaran, Michael C Tanner, and John R Josephson. 1989 · 1989
Earlier work this paper cites.
Question-driven understanding: An integrated theory of story understanding, memory and learning
Ashwin Ram. 1989 · 1989
Earlier work this paper cites.
Grounding in communication
Herbert H Clark, Susan E Brennan, and others. 1991 · 1991
Earlier work this paper cites.
On what we know we don’t know: Explanation, theory, linguistics, and how questions shape them
Sylvain Bromberger. 1992 · 1992
Earlier work this paper cites.
Explanations from intelligent systems: Theoretical foundations and implications for practice
Shirley Gregor and Izak Benbasat. 1999 · 1999
Earlier work this paper cites.
Extracting decision trees from trained neural networks
R Krishnan, G Sivakumar, and P Bhattacharya. 1999 · 1999
Earlier work this paper cites.
Explaining collaborative filtering recommendations. In Proceedings of the 2000 ACM conference on Computer supported cooperative work
Jonathan L Herlocker, Joseph A Konstan, and John Riedl. 2000 · 2000
Earlier work this paper cites.
Intelligibility and accountability: human considerations in context-aware systems
Victoria Bellotti and Keith Edwards. 2001 · 2001
Earlier work this paper cites.
Greedy function approximation: a gradient boosting machine
Jerome H Friedman. 2001 · 2001
Earlier work this paper cites.
Semiotic engineering contributions for designing online help systems. In Proceedings of the 19th annual international conference on Computer documentation
Milene Selbach Silveira, Clarisse Sieckenius de Souza, and Simone DJ Barbosa. 2001 · 2001
Earlier work this paper cites.
Extracting symbolic rules from trained neural network ensembles
Zhi-Hua Zhou, Yuan Jiang, and Shi-Fu Chen. 2003 · 2003
Earlier work this paper cites.
Grounded theory and sensitizing concepts
Glenn A Bowen. 2006 · 2006
Earlier work this paper cites.
Transparency and socially guided machine learning. In 5th Intl. Conf. on Development and Learning (ICDL)
Andrea L Thomaz and Cynthia Breazeal. 2006 · 2006
Earlier work this paper cites.
Toward harnessing user feedback for machine learning. In Proceedings of the 12th international conference on Intelligent user interfaces
Simone Stumpf, Vidya Rajaram, Lida Li, Margaret Burnett, Thomas Dietterich, Erin Sullivan, Russell Drummond, and Jonathan Herlocker. 2007 · 2007
Earlier work this paper cites.
Assistance: the work practices of human administrative assistants and their implications for it and organizations. In Proceedings of the 2008 ACM conference on Computer supported cooperative work
Thomas Erickson, Catalina M Danis, Wendy A Kellogg, and Mary E Helander. 2008 · 2008
Earlier work this paper cites.
Toward establishing trust in adaptive agents. In Proceedings of the 13th international conference on Intelligent user interfaces
Alyssa Glass, Deborah L McGuinness, and Michael Wolverton. 2008 · 2008
Earlier work this paper cites.
Evolving decision trees using oracle guides. In 2009 IEEE Symposium on Computational Intelligence and Data Mining
Ulf Johansson and Lars Niklasson. 2009 · 2009
Earlier work this paper cites.
Assessing demand for intelligibility in context-aware applications. In Proceedings of the 11th international conference on Ubiquitous computing
Brian Y Lim and Anind K Dey. 2009 · 2009
Earlier work this paper cites.
Why and why not explanations improve the intelligibility of context-aware intelligent systems. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Brian Y Lim, Anind K Dey, and Daniel Avrahami. 2009 · 2009
Earlier work this paper cites.
Toolkit to support intelligibility in context-aware applications. In Proceedings of the 12th ACM international conference on Ubiquitous computing
Brian Y Lim and Anind K Dey. 2010 · 2010
Earlier work this paper cites.
Prototype selection for interpretable classification
Jacob Bien, Robert Tibshirani, and others. 2011 · 2011
Earlier work this paper cites.
Accurate intelligible models with pairwise interactions. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Yin Lou, Rich Caruana, Johannes Gehrke, and Giles Hooker. 2013 · 2013
Earlier work this paper cites.
Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2013 · 2013
Earlier work this paper cites.
Power to the people: The role of humans in interactive machine learning
Saleema Amershi, Maya Cakmak, William Bradley Knox, and Todd Kulesza. 2014 · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. 2014 · 2014
Earlier work this paper cites.
A peek into the black box: exploring classifiers by randomization
Andreas Henelius, Kai Puolamäki, Henrik Boström, Lars Asker, and Panagiotis Papapetrou. 2014 · 2014
Earlier work this paper cites.
Auditing algorithms: Research methods for detecting discrimination on internet platforms
Christian Sandvig, Kevin Hamilton, Karrie Karahalios, and Cedric Langbort. 2014 · 2014
Earlier work this paper cites.
Explaining prediction models and individual predictions with feature contributions
Erik Štrumbelj and Igor Kononenko. 2014 · 2014
Earlier work this paper cites.
Basics of qualitative research
Juliet Corbin, Anselm L Strauss, and Anselm Strauss. 2015 · 2015
Earlier work this paper cites.
Algorithmic accountability: Journalistic investigation of computational power structures
Nicholas Diakopoulos. 2015 · 2015
Earlier work this paper cites.
Peeking inside the black box: Visualizing statistical learning with plots of individual conditional expectation
Alex Goldstein, Adam Kapelner, Justin Bleich, and Emil Pitkin. 2015 · 2015
Cited alongside, same era.
Principles of explanatory debugging to personalize interactive machine learning. In Proceedings of the 20th international conference on intelligent user interfaces
Todd Kulesza, Margaret Burnett, Weng-Keen Wong, and Simone Stumpf. 2015 · 2015
Cited alongside, same era.
Visualizing the effects of predictor variables in black box supervised learning models
Daniel W Apley. 2016 · 2016
Cited alongside, same era.
Interacting with predictions: Visual inspection of black-box machine learning models. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems
Josua Krause, Adam Perer, and Kenney Ng. 2016 · 2016
Cited alongside, same era.
Explanation methods in deep learning: Users, values, concerns and challenges
Gabriëlle Ras, Marcel van Gerven, and Pim Haselager. 2018 · 2018
Later among the works it cites.
Anchors: High-precision model-agnostic explanations. In Thirty-Second AAAI Conference on Artificial Intelligence
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2018 · 2018
Later among the works it cites.
Perturbation-Based Explanations of Prediction Models
Marko Robnik-Šikonja and Marko Bohanec. 2018 · 2018
Later among the works it cites.
Exploration and explanation in computational notebooks. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems
Adam Rule, Aurélien Tabard, and James D Hollan. 2018 · 2018
Later among the works it cites.
The challenge of crafting intelligible intelligence
Daniel S Weld and Gagan Bansal. 2018 · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Zachary C Lipton. 2016 · 2016
Cited alongside, same era.
Anh Nguyen, Jason Yosinski, and Jeff Clune. 2016 · 2016
Cited alongside, same era.
Why should i trust you?: Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
Cited alongside, same era.
Learning deep features for discriminative localization. In Proceedings of the IEEE conference on computer vision and pattern recognition
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba. 2016 · 2016
Cited alongside, same era.
Interpretability via model extraction
Osbert Bastani, Carolyn Kim, and Hamsa Bastani. 2017 · 2017
Cited alongside, same era.
How data workers cope with uncertainty: A task characterisation study. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems
Nadia Boukhelifa, Marc-Emmanuel Perrin, Samuel Huron, and James Eagan. 2017 · 2017
Cited alongside, same era.
Towards a rigorous science of interpretable machine learning
Finale Doshi-Velez and Been Kim. 2017 · 2017
Cited alongside, same era.
Understanding black-box predictions via influence functions. In Proceedings of the 34th International Conference on Machine Learning-Volume 70
Pang Wei Koh and Percy Liang. 2017 · 2017
Cited alongside, same era.
Machine Learning as a UX Design Material: How Can We Imagine Beyond Automation, Recommenders, and Reminders?. In 2018 AAAI Spring Symposium Series
Qian Yang. 2018 · 2018
Later among the works it cites.
Interpreting neural network judgments via minimal, stable, and symbolic corrections. In Advances in Neural Information Processing Systems
Xin Zhang, Armando Solar-Lezama, and Rishabh Singh. 2018 · 2018
Later among the works it cites.
Explainable AI for designers: A human-centered perspective on mixed-initiative co-creation. In 2018 IEEE Conference on Computational Intelligence and Games (CIG)
Jichen Zhu, Antonios Liapis, Sebastian Risi, Rafael Bidarra, and G Michael Youngblood. 2018 · 2018
Later among the works it cites.
DALEX: Descriptive Machine Learning EXplanations
2018 · 2019
Later among the works it cites.
H2O Driverless AI
2018 · 2019
Later among the works it cites.
Guidelines for human-AI interaction. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
Saleema Amershi, Dan Weld, Mihaela Vorvoreanu, Adam Fourney, Besmira Nushi, Penny Collisson, Jina Suh, Shamsi Iqbal, Paul N Bennett, Kori Inkpen, and others. 2019 · 2019
Later among the works it cites.
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, Alberto Barbado, Salvador García, Sergio Gil-López, Daniel Molina, Richard Benjamins, and others. 2019 · 2019
Later among the works it cites.
One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques
Vijay Arya, Rachel KE Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C Hoffman, Stephanie Houde, Q Vera Liao, Ronny Luss, Aleksandra Mojsilović, and others. 2019 · 2019
Later among the works it cites.
The effects of example-based explanations in a machine learning interface. In Proceedings of the 24th International Conference on Intelligent User Interfaces
Carrie J Cai, Jonas Jongejan, and Jess Holbrook. 2019 · 2019
Later among the works it cites.
Machine Learning Interpretability: A Survey on Methods and Metrics
Diogo V Carvalho, Eduardo M Pereira, and Jaime S Cardoso. 2019 · 2019
Later among the works it cites.
Explaining Decision-Making Algorithms through UI: Strategies to Help Non-Expert Stakeholders. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
Hao-Fei Cheng, Ruotong Wang, Zheng Zhang, Fiona O’Connell, Terrance Gray, F Maxwell Harper, and Haiyi Zhu. 2019 · 2019
Later among the works it cites.
General Data Protection Regulation
European Commission. 2016 · 2019
Later among the works it cites.
Explaining models: an empirical study of how explanations impact fairness judgment. In Proceedings of the 24th International Conference on Intelligent User Interfaces
Jonathan Dodge, Q Vera Liao, Yunfeng Zhang, Rachel KE Bellamy, and Casey Dugan. 2019 · 2019
Later among the works it cites.
A survey of methods for explaining black box models
Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, and Dino Pedreschi. 2019 · 2019
Later among the works it cites.
Explaining explainable AI
Michael Hind. 2019 · 2019
Later among the works it cites.
Gamut: A design probe to understand how data scientists understand machine learning models. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
Fred Hohman, Andrew Head, Rich Caruana, Robert DeLine, and Steven M Drucker. 2019 · 2019
Later among the works it cites.
Improving fairness in machine learning systems: What do industry practitioners need?. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
Kenneth Holstein, Jennifer Wortman Vaughan, Hal Daumé III, Miro Dudik, and Hanna Wallach. 2019 · 2019
Later among the works it cites.
Will You Accept an Imperfect AI?: Exploring Designs for Adjusting End-user Expectations of AI Systems. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
Rafal Kocielnik, Saleema Amershi, and Paul N Bennett. 2019 · 2019
Later among the works it cites.
A Grounded Interaction Protocol for Explainable Artificial Intelligence. In Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems
Prashan Madumal, Tim Miller, Liz Sonenberg, and Frank Vetere. 2019 · 2019
Later among the works it cites.
How Data Science Workers Work with Data: Discovery, Capture, Curation, Design, Creation. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
Michael Muller, Ingrid Lange, Dakuo Wang, David Piorkowski, Jason Tsay, Q Vera Liao, Casey Dugan, and Thomas Erickson. 2019 · 2019
Later among the works it cites.
Towards explainable artificial intelligence
Wojciech Samek and Klaus-Robert Müller. 2019 · 2019
Later among the works it cites.
Personalized explanation in machine learning
Johanes Schneider and Joshua Handali. 2019 · 2019
Later among the works it cites.
Progressive disclosure: empirically motivated approaches to designing effective transparency. In Proceedings of the 24th International Conference on Intelligent User Interfaces
Aaron Springer and Steve Whittaker. 2019 · 2019
Later among the works it cites.
Designing Theory-Driven User-Centric Explainable AI. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y Lim. 2019 · 2019
Later among the works it cites.
Generalized Linear Rule Models. In International Conference on Machine Learning
Dennis Wei, Sanjeeb Dash, Tian Gao, and Oktay Gunluk. 2019 · 2019
Later among the works it cites.
Explainability scenarios: towards scenario-based XAI design. In Proceedings of the 24th International Conference on Intelligent User Interfaces
Christine T Wolf. 2019 · 2019
Later among the works it cites.
Understanding the Effect of Accuracy on Trust in Machine Learning Models. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
Ming Yin, Jennifer Wortman Vaughan, and Hanna Wallach. 2019 · 2019
Later among the works it cites.
Interpreting cnns via decision trees. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
Quanshi Zhang, Yu Yang, Haotian Ma, and Ying Nian Wu. 2019 · 2019
Later among the works it cites.
Explaining machine learning classifiers through diverse counterfactual explanations. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency
Ramaravind K Mothilal, Amit Sharma, and Chenhao Tan. 2020 · 2020
Closest in time.