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Feature-based methods are commonly used to explain model predictions, but these methods often implicitly assume that interpretable features are readily available.
A survey on explainable artificial intelligence (XAI): towards medical XAI
Erico Tjoa and Cuntai Guan · 1907
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A circumplex model of affect
James A Russell · 1980
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Causes and prevention of laparoscopic bile duct injuries: analysis of 252 cases from a human factors and cognitive psychology perspective
Lawrence W Way, Lygia Stewart, Walter Gantert, Kingsway Liu, Crystine M Lee, Karen Whang, and John G Hunter · 2003
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Entropy-based objective evaluation method for image segmentation
Hui Zhang, Jason E Fritts, and Sally A Goldman · 2003
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Random walks for image segmentation
L. Grady · 2006
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Politeness: is there an east-west divide?
Geoffrey Leech · 2007
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Classification-driven watershed segmentation
Ilya Levner and Hong Zhang · 2007
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The pascal visual object classes (voc) challenge
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2010
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Rationale and use of the critical view of safety in laparoscopic cholecystectomy
Steven M Strasberg and Michael L Brunt · 2010
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A study into politeness strategies and politeness markers in advertisements as persuasive tools
Reza Pishghadam and Safoora Navari · 2012
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Epidemiology of gallbladder disease: cholelithiasis and cancer
Laura M Stinton and Eldon A Shaffer · 2012
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Laparoscopic cholecystectomy: first, do no harm; second, take care of bile duct stones, 2013
George Berci, John Hunter, Leon Morgenstern, Maurice Arregui, Michael Brunt, Brandon Carroll, Michael Edye, David Fermelia, George Ferzli, Frederick Greene, et al · 2013
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A computational approach to politeness with application to social factors
Cristian Danescu-Niculescu-Mizil, Moritz Sudhof, Dan Jurafsky, Jure Leskovec, and Christopher Potts · 2013
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Deep feature synthesis: Towards automating data science endeavors
James Max Kanter and Kalyan Veeramachaneni · 2015
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The cityscapes dataset for semantic urban scene understanding
Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele · 2016
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"why should i trust you?": Explaining the predictions of any classifier, 2016
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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The bases of (im) politeness evaluations: Culture, the moral order and the east-west debate
Helen Spencer-Oatey and Dániel Z Kádár · 2016
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The tum lapchole dataset for the m2cai 2016 workflow challenge
Ralf Stauder, Daniel Ostler, Michael Kranzfelder, Sebastian Koller, Hubertus Feußner, and Nassir Navab · 2016
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Endonet: a deep architecture for recognition tasks on laparoscopic videos
Andru P Twinanda, Sherif Shehata, Didier Mutter, Jacques Marescaux, Michel De Mathelin, and Nicolas Padoy · 2016
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Interpretable classification models for recidivism prediction
Jiaming Zeng, Berk Ustun, and Cynthia Rudin · 2016
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Towards a rigorous science of interpretable machine learning, 2017
Finale Doshi-Velez and Been Kim · 2017
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A unified approach to interpreting model predictions, 2017
Scott Lundberg and Su-In Lee · 2017
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Learning feature engineering for classification
Fatemeh Nargesian, Horst Samulowitz, Udayan Khurana, Elias B. Khalil, and Deepak Turaga · 2017
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Research on politeness in the Spanish-speaking world
María Elena Placencia and Carmen Garcia-Fernandez · 2017
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Axiomatic attribution for deep networks, 2017
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases
Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, and Ronald M Summers · 2017
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When a computer program keeps you in jail: How computers are harming criminal justice
Rebecca Wexler · 2017
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Augmented reality meets computer vision: Efficient data generation for urban driving scenes
Hassan Abu Alhaija, Siva Karthik Mustikovela, Lars Mescheder, Andreas Geiger, and Carsten Rother · 2018
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The photometric lsst astronomical time-series classification challenge (plasticc): Data set
Tarek Allam Jr, Anita Bahmanyar, Rahul Biswas, Mi Dai, Lluís Galbany, Renée Hložek, Emille EO Ishida, Saurabh W Jha, David O Jones, Richard Kessler, et al · 2018
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On the robustness of interpretability methods
David Alvarez-Melis and Tommi S. Jaakkola · 2018
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Visual analytics for explainable deep learning
Jaegul Choo and Shixia Liu · 2018
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Student learning benefits of a mixed-reality teacher awareness tool in ai-enhanced classrooms
Kenneth Holstein, Bruce M. McLaren, and Vincent Aleven · 2018
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Towards explainable deep learning for credit lending: A case study
Ceena Modarres, Mark Ibrahim, Melissa Louie, and John Paisley · 2018
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Explanations as mechanisms for supporting algorithmic transparency
Emilee Rader, Kelley Cotter, and Janghee Cho · 2018
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The photometric lsst astronomical time-series classification challenge (plasticc): Data set, 2018
The PLAsTiCC Team, Tarek Allam Jr. au2, Anita Bahmanyar, Rahul Biswas, Mi Dai, Lluís Galbany, Renée Hložek, Emille E. O. Ishida, Saurabh W. Jha, David O. Jones, Richard Kessler, Michelle Lochner, Ashish A. Mahabal, Alex I. Malz, Kaisey S. Mandel, Juan Rafael Martínez-Galarza, Jason D. McEwen, Daniel Muthukrishna, Gautham Narayan, Hiranya Peiris, Christina M. Peters, Kara Ponder, Christian N. Setzer, The LSST Dark Energy Science Collaboration, The LSST Transients, and Variable Stars Science Collaboration · 2018
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Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai, 2019
Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador García, Sergio Gil-López, Daniel Molina, Richard Benjamins, Raja Chatila, and Francisco Herrera · 2019
Cited alongside, same era.
Eraser: A benchmark to evaluate rationalized nlp models
Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani, Eric Lehman, Caiming Xiong, Richard Socher, and Byron C Wallace · 2019
Cited alongside, same era.
Surgical procedural map scoring for decision-making in laparoscopic cholecystectomy
Daniel A Hashimoto, C Gustaf Axelsson, Cara B Jones, Roy Phitayakorn, Emil Petrusa, Sophia K McKinley, Denise Gee, and Carla Pugh · 2019
Cited alongside, same era.
A benchmark for interpretability methods in deep neural networks, 2019
Sara Hooker, Dumitru Erhan, Pieter-Jan Kindermans, and Been Kim · 2019
Cited alongside, same era.
Predictive policing: Review of benefits and drawbacks
Albert Meijer and Martijn Wessels · 2019
Openxai: Towards a transparent evaluation of model explanations
Chirag Agarwal, Satyapriya Krishna, Eshika Saxena, Martin Pawelczyk, Nari Johnson, Isha Puri, Marinka Zitnik, and Himabindu Lakkaraju · 2022
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TorchXRayVision: A library of chest X-ray datasets and models
Joseph Paul Cohen, Joseph D. Viviano, Paul Bertin, Paul Morrison, Parsa Torabian, Matteo Guarrera, Matthew P Lungren, Akshay Chaudhari, Rupert Brooks, Mohammad Hashir, and Hadrien Bertrand · 2022
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Full w CDM w\text{CDM} analysis of KiDS-1000 weak lensing maps using deep learning
Janis Fluri, Tomasz Kacprzak, Aurelien Lucchi, Aurel Schneider, Alexandre Refregier, and Thomas Hofmann · 2022
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Bertopic: Neural topic modeling with a class-based tf-idf procedure
Maarten Grootendorst · 2022
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Artificial intelligence for intraoperative guidance: using semantic segmentation to identify surgical anatomy during laparoscopic cholecystectomy
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Cited alongside, same era.
Weak lensing cosmology with convolutional neural networks on noisy data
Dezső Ribli, Bálint Ármin Pataki, José Manuel Zorrilla Matilla, Daniel Hsu, Zoltán Haiman, and István Csabai · 2019
Cited alongside, same era.
Role of fairness, accountability, and transparency in algorithmic affordance
Donghee Shin and Yong Jin Park · 2019
Cited alongside, same era.
explainer: A visual analytics framework for interactive and explainable machine learning
Thilo Spinner, Udo Schlegel, Hanna Schäfer, and Mennatallah El-Assady · 2019
Cited alongside, same era.
Lsst: From science drivers to reference design and anticipated data products
Željko Ivezić, Steven M. Kahn, J. Anthony Tyson, Bob Abel, Emily Acosta, Robyn Allsman, David Alonso, Yusra AlSayyad, Scott F. Anderson, John Andrew, James Roger P. Angel, George Z. Angeli, Reza Ansari, Pierre Antilogus, Constanza Araujo, Robert Armstrong, Kirk T. Arndt, Pierre Astier, Éric Aubourg, Nicole Auza, Tim S. Axelrod, Deborah J. Bard, Jeff D. Barr, Aurelian Barrau, James G. Bartlett, Amanda E. Bauer, Brian J. Bauman, Sylvain Baumont, Ellen Bechtol, Keith Bechtol, Andrew C. Becker, Jacek Becla, Cristina Beldica, Steve Bellavia, Federica B. Bianco, Rahul Biswas, Guillaume Blanc, Jonathan Blazek, Roger D. Blandford, Josh S. Bloom, Joanne Bogart, Tim W. Bond, Michael T. Booth, Anders W. Borgland, Kirk Borne, James F. Bosch, Dominique Boutigny, Craig A. Brackett, Andrew Bradshaw, William Nielsen Brandt, Michael E. Brown, James S. Bullock, Patricia Burchat, David L. Burke, Gianpietro Cagnoli, Daniel Calabrese, Shawn Callahan, Alice L. Callen, Jeffrey L. Carlin, Erin L. Carlson, Srinivasan Chandrasekharan, Glenaver Charles-Emerson, Steve Chesley, Elliott C. Cheu, Hsin-Fang Chiang, James Chiang, Carol Chirino, Derek Chow, David R. Ciardi, Charles F. Claver, Johann Cohen-Tanugi, Joseph J. Cockrum, Rebecca Coles, Andrew J. Connolly, Kem H. Cook, Asantha Cooray, Kevin R. Covey, Chris Cribbs, Wei Cui, Roc Cutri, Philip N. Daly, Scott F. Daniel, Felipe Daruich, Guillaume Daubard, Greg Daues, William Dawson, Francisco Delgado, Alfred Dellapenna, Robert de Peyster, Miguel de Val-Borro, Seth W. Digel, Peter Doherty, Richard Dubois, Gregory P. Dubois-Felsmann, Josef Durech, Frossie Economou, Tim Eifler, Michael Eracleous, Benjamin L. Emmons, Angelo Fausti Neto, Henry Ferguson, Enrique Figueroa, Merlin Fisher-Levine, Warren Focke, Michael D. Foss, James Frank, Michael D. Freemon, Emmanuel Gangler, Eric Gawiser, John C. Geary, Perry Gee, Marla Geha, Charles J. B. Gessner, Robert R. Gibson, D. Kirk Gilmore, Thomas Glanzman, William Glick, Tatiana Goldina, Daniel A. Goldstein, Iain Goodenow, Melissa L. Graham, William J. Gressler, Philippe Gris, Leanne P. Guy, Augustin Guyonnet, Gunther Haller, Ron Harris, Patrick A. Hascall, Justine Haupt, Fabio Hernandez, Sven Herrmann, Edward Hileman, Joshua Hoblitt, John A. Hodgson, Craig Hogan, James D. Howard, Dajun Huang, Michael E. Huffer, Patrick Ingraham, Walter R. Innes, Suzanne H. Jacoby, Bhuvnesh Jain, Fabrice Jammes, M. James Jee, Tim Jenness, Garrett Jernigan, Darko Jevremović, Kenneth Johns, Anthony S. Johnson, Margaret W. G. Johnson, R. Lynne Jones, Claire Juramy-Gilles, Mario Jurić, Jason S. Kalirai, Nitya J. Kallivayalil, Bryce Kalmbach, Jeffrey P. Kantor, Pierre Karst, Mansi M. Kasliwal, Heather Kelly, Richard Kessler, Veronica Kinnison, David Kirkby, Lloyd Knox, Ivan V. Kotov, Victor L. Krabbendam, K. Simon Krughoff, Petr Kubánek, John Kuczewski, Shri Kulkarni, John Ku, Nadine R. Kurita, Craig S. Lage, Ron Lambert, Travis Lange, J. Brian Langton, Laurent Le Guillou, Deborah Levine, Ming Liang, Kian-Tat Lim, Chris J. Lintott, Kevin E. Long, Margaux Lopez, Paul J. Lotz, Robert H. Lupton, Nate B. Lust, Lauren A. MacArthur, Ashish Mahabal, Rachel Mandelbaum, Thomas W. Markiewicz, Darren S. Marsh, Philip J. Marshall, Stuart Marshall, Morgan May, Robert McKercher, Michelle McQueen, Joshua Meyers, Myriam Migliore, Michelle Miller, David J. Mills, Connor Miraval, Joachim Moeyens, Fred E. Moolekamp, David G. Monet, Marc Moniez, Serge Monkewitz, Christopher Montgomery, Christopher B. Morrison, Fritz Mueller, Gary P. Muller, Freddy Muñoz Arancibia, Douglas R. Neill, Scott P. Newbry, Jean-Yves Nief, Andrei Nomerotski, Martin Nordby, Paul O’Connor, John Oliver, Scot S. Olivier, Knut Olsen, William O’Mullane, Sandra Ortiz, Shawn Osier, Russell E. Owen, Reynald Pain, Paul E. Palecek, John K. Parejko, James B. Parsons, Nathan M. Pease, J. Matt Peterson, John R. Peterson, Donald L. Petravick, M. E. Libby Petrick, Cathy E. Petry, Francesco Pierfederici, Stephen Pietrowicz, Rob Pike, Philip A. Pinto, Raymond Plante, Stephen Plate, Joel P. Plutchak, Paul A. Price, Michael Prouza, Veljko Radeka, Jayadev Rajagopal, Andrew P. Rasmussen, Nicolas Regnault, Kevin A. Reil, David J. Reiss, Michael A. Reuter, Stephen T. Ridgway, Vincent J. Riot, Steve Ritz, Sean Robinson, William Roby, Aaron Roodman, Wayne Rosing, Cecille Roucelle, Matthew R. Rumore, Stefano Russo, Abhijit Saha, Benoit Sassolas, Terry L. Schalk, Pim Schellart, Rafe H. Schindler, Samuel Schmidt, Donald P. Schneider, Michael D. Schneider, William Schoening, German Schumacher, Megan E. Schwamb, Jacques Sebag, Brian Selvy, Glenn H. Sembroski, Lynn G. Seppala, Andrew Serio, Eduardo Serrano, Richard A. Shaw, Ian Shipsey, Jonathan Sick, Nicole Silvestri, Colin T. Slater, J. Allyn Smith, R. Chris Smith, Shahram Sobhani, Christine Soldahl, Lisa Storrie-Lombardi, Edward Stover, Michael A. Strauss, Rachel A. Street, Christopher W. Stubbs, Ian S. Sullivan, Donald Sweeney, John D. Swinbank, Alexander Szalay, Peter Takacs, Stephen A. Tether, Jon J. Thaler, John Gregg Thayer, Sandrine Thomas, Adam J. Thornton, Vaikunth Thukral, Jeffrey Tice, David E. Trilling, Max Turri, Richard Van Berg, Daniel Vanden Berk, Kurt Vetter, Francoise Virieux, Tomislav Vucina, William Wahl, Lucianne Walkowicz, Brian Walsh, Christopher W. Walter, Daniel L. Wang, Shin-Yawn Wang, Michael Warner, Oliver Wiecha, Beth Willman, Scott E. Winters, David Wittman, Sidney C. Wolff, W. Michael Wood-Vasey, Xiuqin Wu, Bo Xin, Peter Yoachim, and Hu Zhan · 2019
Cited alongside, same era.
Explanation in ai and law: Past, present and future
Katie Atkinson, Trevor Bench-Capon, and Danushka Bollegala · 2020
Cited alongside, same era.
GoEmotions: A dataset of fine-grained emotions
Dorottya Demszky, Dana Movshovitz-Attias, Jeongwoo Ko, Alan Cowen, Gaurav Nemade, and Sujith Ravi · 2020
Cited alongside, same era.
Autogluon-tabular: Robust and accurate automl for structured data, 2020
Nick Erickson, Jonas Mueller, Alexander Shirkov, Hang Zhang, Pedro Larroy, Mu Li, and Alexander Smola · 2020
Cited alongside, same era.
Amin Madani, Babak Namazi, Maria S Altieri, Daniel A Hashimoto, Angela Maria Rivera, Philip H Pucher, Allison Navarrete-Welton, Ganesh Sankaranarayanan, L Michael Brunt, Allan Okrainec, et al · 2022
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On the granularity of explanations in model agnostic nlp interpretability
Yves Rychener, Xavier Renard, Djamé Seddah, Pascal Frossard, and Marcin Detyniecki · 2022
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The need for interpretable features: Motivation and taxonomy, 2022
Alexandra Zytek, Ignacio Arnaldo, Dongyu Liu, Laure Berti-Equille, and Kalyan Veeramachaneni · 2022
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Evaluating explainability for graph neural networks
Chirag Agarwal, Owen Queen, Himabindu Lakkaraju, and Marinka Zitnik · 2023
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Artificial intelligence to automate the systematic review of scientific literature
José de la Torre-López, Aurora Ramírez, and José Raúl Romero · 2023
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Explainable ai (xai): Core ideas, techniques, and solutions
Rudresh Dwivedi, Devam Dave, Het Naik, Smiti Singhal, Rana Omer, Pankesh Patel, Bin Qian, Zhenyu Wen, Tejal Shah, Graham Morgan, et al · 2023
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Craft: Concept recursive activation factorization for explainability
Thomas Fel, Agustin Picard, Louis Bethune, Thibaut Boissin, David Vigouroux, Julien Colin, Rémi Cadène, and Thomas Serre · 2023
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Explaining why the computer says no: Algorithmic transparency affects the perceived trustworthiness of automated decision-making
Stephan Grimmelikhuijsen · 2023
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What is the educational value and clinical utility of artifcial intelligence for intraoperative and postoperative video analysis? a survey of surgeons and trainees
Mohamed Saif Hameed, Simon Laplante, Caterina Masino, Muhammad Khalid, Haochi Zhang, Sergey Protserov, Jaryd Hunter, Pouria Mashouri, Andras Fecso, Michael Brudno, and Amin Madani · 2023
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Comparing styles across languages
Shreya Havaldar, Matthew Pressimone, Eric Wong, and Lyle Ungar · 2023
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Multilingual language models are not multicultural: A case study in emotion
Shreya Havaldar, Bhumika Singhal, Sunny Rai, Langchen Liu, Sharath Chandra Guntuku, and Lyle Ungar · 2023
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Quantus: An explainable ai toolkit for responsible evaluation of neural network explanations and beyond
Anna Hedström, Leander Weber, Daniel Krakowczyk, Dilyara Bareeva, Franz Motzkus, Wojciech Samek, Sebastian Lapuschkin, and Marina Marina M.-C. Höhne · 2023
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CosmoGridV1: a simulated LambdaCDM theory prediction for map-level cosmological inference
Tomasz Kacprzak, Janis Fluri, Aurel Schneider, Alexandre Refregier, and Joachim Stadel · 2023
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Segment anything, 2023
Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dollár, and Ross Girshick · 2023
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Interpretable and explainable machine learning: A methods-centric overview with concrete examples
Ričards Marcinkevičs and Julia E Vogt · 2023
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Almanacs: A simulatability benchmark for language model explainability, 2023
Edmund Mills, Shiye Su, Stuart Russell, and Scott Emmons · 2023
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From anecdotal evidence to quantitative evaluation methods: A systematic review on evaluating explainable ai
Meike Nauta, Jan Trienes, Shreyasi Pathak, Elisa Nguyen, Michelle Peters, Yasmin Schmitt, Jörg Schlötterer, Maurice Van Keulen, and Christin Seifert · 2023
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Toward transparent ai: A survey on interpreting the inner structures of deep neural networks, 2023
Tilman Räuker, Anson Ho, Stephen Casper, and Dylan Hadfield-Menell · 2023
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Explainable ai (xai): A systematic meta-survey of current challenges and future opportunities
Waddah Saeed and Christian Omlin · 2023
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Deepbio: an automated and interpretable deep-learning platform for high-throughput biological sequence prediction, functional annotation and visualization analysis
Ruheng Wang, Yi Jiang, Junru Jin, Chenglin Yin, Haoqing Yu, Fengsheng Wang, Jiuxin Feng, Ran Su, Kenta Nakai, Quan Zou, et al · 2023
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Beyond explaining: Opportunities and challenges of xai-based model improvement
Leander Weber, Sebastian Lapuschkin, Alexander Binder, and Wojciech Samek · 2023
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Sum-of-parts models: Faithful attributions for groups of features, 2023
Weiqiu You, Helen Qu, Marco Gatti, Bhuvnesh Jain, and Eric Wong · 2023
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Toolqa: A dataset for llm question answering with external tools, 2023
Yuchen Zhuang, Yue Yu, Kuan Wang, Haotian Sun, and Chao Zhang · 2023
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On evaluating explanation utility for human-ai decision making in nlp
Fateme Hashemi Chaleshtori, Atreya Ghosal, Alexander Gill, Purbid Bambroo, and Ana Marasović · 2024
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Building knowledge-guided lexica to model cultural variation
Shreya Havaldar, Salvatore Giorgi, Sunny Rai, Thomas Talhelm, Sharath Chandra Guntuku, and Lyle Ungar · 2024
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Evaluating groups of features via consistency, contiguity, and stability
Chaehyeon Kim, Weiqiu You, Shreya Havaldar, and Eric Wong · 2024
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Drac 2022: A public benchmark for diabetic retinopathy analysis on ultra-wide optical coherence tomography angiography images
Bo Qian, Hao Chen, Xiangning Wang, Zhouyu Guan, Tingyao Li, Yixiao Jin, Yilan Wu, Yang Wen, Haoxuan Che, Gitaek Kwon, et al · 2024
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An explanation of what, why, and how of explainable ai (xai)
Abhishek Rai · 2024
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Find: A function description benchmark for evaluating interpretability methods
Sarah Schwettmann, Tamar Shaham, Joanna Materzynska, Neil Chowdhury, Shuang Li, Jacob Andreas, David Bau, and Antonio Torralba · 2024
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Heterogeneity and predictors of the effects of ai assistance on radiologists
Feiyang Yu, Alex Moehring, Oishi Banerjee, Tobias Salz, Nikhil Agarwal, and Pranav Rajpurkar · 2024
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Evaluating the quality of machine learning explanations: A survey on methods and metrics
Jianlong Zhou, Amir H. Gandomi, Fang Chen, and Andreas Holzinger · 2079
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