European Union regulations on algorithmic decision-making and a "right to explanation"

28 June 2016

Seth Flaxman

Papers citing "European Union regulations on algorithmic decision-making and a "right to explanation""

50 / 217 papers shown

Title
Sparsity in Optimal Randomized Classification Trees R. Blanquero E. Carrizosa Cristina Molero-Río Dolores Romero Morales 33 46 0 21 Feb 2020
Post-Comparison Mitigation of Demographic Bias in Face Recognition Using Fair Score Normalization Philipp Terhörst J. Kolf Naser Damer Florian Kirchbuchner Arjan Kuijper 19 56 0 10 Feb 2020
Implementations in Machine Ethics: A Survey Suzanne Tolmeijer Markus Kneer Cristina Sarasua Markus Christen Abraham Bernstein FaML 26 71 0 21 Jan 2020
On Interpretability of Artificial Neural Networks: A Survey Fenglei Fan Jinjun Xiong Mengzhou Li Ge Wang AAML AI4CE 43 301 0 08 Jan 2020
Revealing Neural Network Bias to Non-Experts Through Interactive Counterfactual Examples Chelsea M. Myers Evan Freed Luis Fernando Laris Pardo Anushay Furqan S. Risi Jichen Zhu CML 18 12 0 07 Jan 2020
Transparent Classification with Multilayer Logical Perceptrons and Random Binarization Zhuo Wang Wei Zhang Ning Liu Jianyong Wang 19 29 0 10 Dec 2019
Counterfactual Explanation Algorithms for Behavioral and Textual Data Yanou Ramon David Martens F. Provost Theodoros Evgeniou FAtt 31 87 0 04 Dec 2019
Domain Knowledge Aided Explainable Artificial Intelligence for Intrusion Detection and Response Sheikh Rabiul Islam W. Eberle S. Ghafoor Ambareen Siraj Mike Rogers 19 39 0 22 Nov 2019
Algorithmic decision-making in AVs: Understanding ethical and technical concerns for smart cities H. S. M. Lim Araz Taeihagh 27 82 0 29 Oct 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 41 6,125 0 22 Oct 2019
Towards Best Practice in Explaining Neural Network Decisions with LRP M. Kohlbrenner Alexander Bauer Shinichi Nakajima Alexander Binder Wojciech Samek Sebastian Lapuschkin 22 147 0 22 Oct 2019
AI for Explaining Decisions in Multi-Agent Environments Sarit Kraus A. Azaria J. Fiosina Maike Greve Noam Hazon L. Kolbe Tim-Benjamin Lembcke J. P. Müller Sören Schleibaum M. Vollrath 20 40 0 10 Oct 2019
What does it mean to solve the problem of discrimination in hiring? Social, technical and legal perspectives from the UK on automated hiring systems Javier Sánchez-Monedero L. Dencik L. Edwards 11 131 0 28 Sep 2019
Towards Explainable Artificial Intelligence Wojciech Samek K. Müller XAI 32 437 0 26 Sep 2019
Measure Contribution of Participants in Federated Learning Guan Wang Charlie Xiaoqian Dang Ziye Zhou FedML 47 195 0 17 Sep 2019
Preventing the Generation of Inconsistent Sets of Classification Rules T. Z. Miranda D. B. Sardinha R. Cerri 18 2 0 23 Aug 2019
The Price of Interpretability Dimitris Bertsimas A. Delarue Patrick Jaillet Sébastien Martin 23 33 0 08 Jul 2019
On Explaining Machine Learning Models by Evolving Crucial and Compact Features M. Virgolin Tanja Alderliesten Peter A. N. Bosman 19 28 0 04 Jul 2019
Machine Learning Testing: Survey, Landscapes and Horizons Jie M. Zhang Mark Harman Lei Ma Yang Liu VLM AILaw 39 741 0 19 Jun 2019
Incorporating Priors with Feature Attribution on Text Classification Frederick Liu Besim Avci FAtt FaML 36 120 0 19 Jun 2019
Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees Summer Devlin Chandan Singh W. James Murdoch Bin Yu FAtt 19 14 0 18 May 2019
How Case Based Reasoning Explained Neural Networks: An XAI Survey of Post-Hoc Explanation-by-Example in ANN-CBR Twins Mark T. Keane Eoin M. Kenny 13 82 0 17 May 2019
From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices Jessica Morley Luciano Floridi Libby Kinsey Anat Elhalal 16 56 0 15 May 2019
Interpretability with Accurate Small Models Abhishek Ghose Balaraman Ravindran 23 1 0 04 May 2019
Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Values Approximation Marco Ancona Cengiz Öztireli Markus Gross FAtt TDI 30 223 0 26 Mar 2019
Explaining individual predictions when features are dependent: More accurate approximations to Shapley values K. Aas Martin Jullum Anders Løland FAtt TDI 16 606 0 25 Mar 2019
Copying Machine Learning Classifiers Irene Unceta Jordi Nin O. Pujol 14 18 0 05 Mar 2019
Unmasking Clever Hans Predictors and Assessing What Machines Really Learn Sebastian Lapuschkin S. Wäldchen Alexander Binder G. Montavon Wojciech Samek K. Müller 17 996 0 26 Feb 2019
Towards Automatic Concept-based Explanations Amirata Ghorbani James Wexler James Zou Been Kim FAtt LRM 38 19 0 07 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
SecureBoost: A Lossless Federated Learning Framework Kewei Cheng Tao Fan Yilun Jin Yang Liu Tianjian Chen Dimitrios Papadopoulos Qiang Yang FedML 25 578 0 25 Jan 2019
Explaining Explanations to Society Leilani H. Gilpin Cecilia Testart Nathaniel Fruchter Julius Adebayo XAI 24 34 0 19 Jan 2019
Fair and Unbiased Algorithmic Decision Making: Current State and Future Challenges Songül Tolan FaML 24 31 0 15 Jan 2019
Interpretable machine learning: definitions, methods, and applications W. James Murdoch Chandan Singh Karl Kumbier R. Abbasi-Asl Bin-Xia Yu XAI HAI 52 1,423 0 14 Jan 2019
Metrics for Explainable AI: Challenges and Prospects R. Hoffman Shane T. Mueller Gary Klein Jordan Litman XAI 10 719 0 11 Dec 2018
A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems Sina Mohseni Niloofar Zarei Eric D. Ragan 36 102 0 28 Nov 2018
Abduction-Based Explanations for Machine Learning Models Alexey Ignatiev Nina Narodytska Sasha Rubin FAtt 20 219 0 26 Nov 2018
Sanity Checks for Saliency Maps Julius Adebayo Justin Gilmer M. Muelly Ian Goodfellow Moritz Hardt Been Kim FAtt AAML XAI 67 1,931 0 08 Oct 2018
Stakeholders in Explainable AI Alun D. Preece Daniel Harborne Dave Braines Richard J. Tomsett Supriyo Chakraborty 15 154 0 29 Sep 2018
Hows and Whys of Artificial Intelligence for Public Sector Decisions: Explanation and Evaluation Alun D. Preece Rob Ashelford Harry Armstrong Dave Braines 22 6 0 28 Sep 2018
Bias Amplification in Artificial Intelligence Systems Kirsten Lloyd 6 43 0 20 Sep 2018
Imparting Interpretability to Word Embeddings while Preserving Semantic Structure Lutfi Kerem Senel Ihsan Utlu Furkan Şahinuç H. Ozaktas Aykut Kocc 32 14 0 19 Jul 2018
RuleMatrix: Visualizing and Understanding Classifiers with Rules Yao Ming Huamin Qu E. Bertini FAtt 20 214 0 17 Jul 2018
Optimal Piecewise Local-Linear Approximations Kartik Ahuja W. Zame M. Schaar FAtt 27 1 0 27 Jun 2018
Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems Richard J. Tomsett Dave Braines Daniel Harborne Alun D. Preece Supriyo Chakraborty FaML 29 164 0 20 Jun 2018
Hierarchical interpretations for neural network predictions Chandan Singh W. James Murdoch Bin Yu 31 145 0 14 Jun 2018
Assessing the impact of machine intelligence on human behaviour: an interdisciplinary endeavour Emilia Gómez Carlos Castillo V. Charisi V. Dahl G. Deco ... Núria Sebastián Xavier Serra Joan Serrà Songül Tolan Karina Vold 19 11 0 07 Jun 2018
Producing radiologist-quality reports for interpretable artificial intelligence William Gale Luke Oakden-Rayner G. Carneiro A. Bradley L. Palmer MedIm 19 46 0 01 Jun 2018
Explaining Explanations: An Overview of Interpretability of Machine Learning Leilani H. Gilpin David Bau Ben Z. Yuan Ayesha Bajwa Michael A. Specter Lalana Kagal XAI 40 1,842 0 31 May 2018
Regularization Learning Networks: Deep Learning for Tabular Datasets Ira Shavitt E. Segal AI4CE 26 20 0 16 May 2018