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Despite widespread calls for transparent artificial intelligence systems, the term is too overburdened with disparate meanings to express precise policy aims or to orient concrete lines of research.
Assumptions implicit in remote sensing data acquisition and analysis
MJ Duggin and CJ Robinove · 1990
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Artificial neural networks: opening the black box
Judith E Dayhoff and James M DeLeo · 2001
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Privacy policies as decision-making tools: an evaluation of online privacy notices
Carlos Jensen and Colin M. Potts · 2004
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Explanation in recommender systems
David McSherry · 2005
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A survey on transparency tools for enhancing privacy
Hans Hedbom · 2009
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Causality
Judea Pearl · 2009
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Explanation and trust: what to tell the user in security and ai?
Wolter Pieters · 2011
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From normativity to responsibility
Joseph Raz · 2011
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Depth: An Account of Scientific Explanation
M. Strevens · 2011
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Gone in 15 seconds: The limits of privacy transparency and control
Alessandro Acquisti, Idris Adjerid, and Laura Brandimarte · 2013
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Big data, big questions| working within a black box: Transparency in the collection and production of big twitter data
Kevin Driscoll and Shawn Walker · 2014
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Respecting people and respecting privacy
L Jean Camp · 2015
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Customer data: Designing for transparency and trust
Timothy Morey, Theodore Forbath, and Allison Schoop · 2015
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Artifact review and badging, 2016
ACM · 2016
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Can we open the black box of ai?
Davide Castelvecchi · 2016
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Algorithmic transparency via quantitative input influence: Theory and experiments with learning systems
Anupam Datta, Shayak Sen, and Yair Zick · 2016
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Generating visual explanations, 2016
Lisa Anne Hendricks, Zeynep Akata, Marcus Rohrbach, Jeff Donahue, Bernt Schiele, and Trevor Darrell · 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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From static to interactive: transforming data visualization to improve transparency
Tracey L Weissgerber, Vesna D Garovic, Marko Savic, Stacey J Winham, and Natasa M Milic · 2016
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What does explainable ai really mean? a new conceptualization of perspectives, 2017
Derek Doran, Sarah Schulz, and Tarek R. Besold · 2017
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Intelligibility in the face of uncertainty, 2017
Brandin Hanson Knowles · 2017
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Understanding black-box predictions via influence functions, 2017
Pang Wei Koh and Percy Liang · 2017
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Counterfactual explanations without opening the black box: Automated decisions and the gdpr
Sandra Wachter, Brent Mittelstadt, and Chris Russell · 2017
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Challenges for transparency, 2017
Adrian Weller · 2017
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Peeking inside the black-box: a survey on explainable artificial intelligence (xai)
Amina Adadi and Mohammed Berrada · 2018
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Acm code of ethics and professional conduct, 2018
DW Gotterbarn, Bo Brinkman, Catherine Flick, Michael S Kirkpatrick, Keith Miller, Kate Vazansky, and Marty J Wolf · 2018
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State of the art: Reproducibility in artificial intelligence, 2018
Odd Erik Gundersen and Sigbjørn Kjensmo · 2018
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Data transparency: Concerns and prospects [point of view]
Nikolaos Laoutaris · 2018
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Troubling trends in machine learning scholarship, 2018
Zachary C. Lipton and Jacob Steinhardt · 2018
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The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery
Zachary C Lipton · 2018
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“meaningful information” and the right to explanation, 2018
Andrew Selbst and Julia Powles · 2018
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What are you hiding? algorithmic transparency and user perceptions, 2018
Aaron Springer and Steve Whittaker · 2018
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Distill-and-compare: Auditing black-box models using transparent model distillation
Sarah Tan, Rich Caruana, Giles Hooker, and Yin Lou · 2018
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Fairness and accountability design needs for algorithmic support in high-stakes public sector decision-making
Michael Veale, Max Van Kleek, and Reuben Binns · 2018
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General data protection regulation
Paul Voigt · 2018
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Ethics as an escape from regulation. from “ethics-washing” to ethics-shopping?
Ben Wagner · 2018
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How Is ‘Transparency’ Understood By Legal Scholars And The Machine Learning Community?
Karen Yeung and Adrian Weller · 2018
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Transparency and replicability in qualitative research: The case of interviews with elite informants
Herman Aguinis and Angelo M Solarino · 2019
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Transparent, scrutable and explainable user models for personalized recommendation
Krisztian Balog, Filip Radlinski, and Shushan Arakelyan · 2019
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Quantifying transparency of machine learning systems through analysis of contributions, 2019
Iain Barclay, Alun Preece, Ian Taylor, and Dinesh Verma · 2019
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Don’t let industry write the rules for ai
Yochai Benkler · 2019
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Data transparency with blockchain and ai ethics
Elisa Bertino, Ahish Kundu, and Zehra Sura · 2019
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Redefining data transparency: A multidimensional approach
Elisa Bertino, Shawn Merrill, Alina Nesen, and Christine Utz · 2019
Transparency as threat at the intersection of artificial intelligence and cyberbiosecurity
Sara B Jordan, Samantha L Fenn, and Benjamin B Shannon · 2020
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Against interpretability: a critical examination of the interpretability problem in machine learning
Maya Krishnan · 2020
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Child-friendly transparency of data processing in the eu: from legal requirements to platform policies
Ingrida Milkaite and Eva Lievens · 2020
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The case for usable ai: What industry professionals make of academic ai in video games
Johannes Pfau, Jan David Smeddinck, and Rainer Malaka · 2020
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Transparency and trust in artificial intelligence systems
Philipp Schmidt, Felix Biessmann, and Timm Teubner · 2020
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Accurate, reliable and fast robustness evaluation
Wieland Brendel, Jonas Rauber, Matthias Kümmerer, Ivan Ustyuzhaninov, and Matthias Bethge · 2019
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Fairness and transparency in ranking
Carlos Castillo · 2019
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A survey of explainable AI terminology
Miruna-Adriana Clinciu and Helen Hastie · 2019
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Computational reproducibility via containers in psychology
April Clyburne-Sherin, Xu Fei, and Seth Ariel Green · 2019
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Techniques for interpretable machine learning
Mengnan Du, Ninghao Liu, and Xia Hu · 2019
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Automated rationale generation: A technique for explainable ai and its effects on human perceptions, 2019
Upol Ehsan, Pradyumna Tambwekar, Larry Chan, Brent Harrison, and Mark Riedl · 2019
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Ehsan Toreini, Mhairi Aitken, Kovila Coopamootoo, Karen Elliott, Carlos Gonzalez Zelaya, and Aad Van Moorsel · 2020
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A human-centered agenda for intelligible machine learning, 2020
Jennifer Wortman Vaughan and Hanna Wallach · 2020
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Cobra: A cli tool to create and share reproducible projects
Lars Vögtlin, Vinaychandran Pondenkandath, and Rolf Ingold · 2020
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Factors influencing perceived fairness in algorithmic decision-making: Algorithm outcomes, development procedures, and individual differences
Ruotong Wang, F Maxwell Harper, and Haiyi Zhu · 2020
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Artificial Intelligence and Transparency: Opening the Black Box
Thomas Wischmeyer · 2020
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Discerning the effect of privacy information transparency on privacy fatigue in e-government
Divine Q Agozie and Tugberk Kaya · 2021
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Privacy policies over time: Curation and analysis of a million-document dataset
Ryan Amos, Gunes Acar, Elena Lucherini, Mihir Kshirsagar, Arvind Narayanan, and Jonathan Mayer · 2021
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A framework for fostering transparency in shared artificial intelligence models by increasing visibility of contributions
Iain Barclay, Harrison Taylor, Alun Preece, Ian Taylor, Dinesh Verma, and Geeth de Mel · 2021
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Using jupyter for reproducible scientific workflows
Marijan Beg, Juliette Taka, Thomas Kluyver, Alexander Konovalov, Min Ragan-Kelley, Nicolas M Thiéry, and Hans Fangohr · 2021
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Uncertainty as a form of transparency: Measuring, communicating, and using uncertainty
Umang Bhatt, Javier Antorán, Yunfeng Zhang, Q Vera Liao, Prasanna Sattigeri, Riccardo Fogliato, Gabrielle Melançon, Ranganath Krishnan, Jason Stanley, Omesh Tickoo, et al · 2021
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Beyond fairness metrics: Roadblocks and challenges for ethical ai in practice, 2021
Jiahao Chen, Victor Storchan, and Eren Kurshan · 2021
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Expanding explainability: Towards social transparency in ai systems
Upol Ehsan, Q. Vera Liao, Michael Muller, Mark O. Riedl, and Justin D. Weisz · 2021
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Datasheets for datasets
Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, and Kate Crawford · 2021
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Tilt: A gdpr-aligned transparency information language and toolkit for practical privacy engineering
Elias Grünewald and Frank Pallas · 2021
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Impossible explanations? beyond explainable ai in the gdpr from a covid-19 use case scenario
Ronan Hamon, Henrik Junklewitz, Gianclaudio Malgieri, Paul De Hert, Laurent Beslay, and Ignacio Sanchez · 2021
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Towards accountability for machine learning datasets: Practices from software engineering and infrastructure
Ben Hutchinson, Andrew Smart, Alex Hanna, Emily Denton, Christina Greer, Oddur Kjartansson, Parker Barnes, and Margaret Mitchell · 2021
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The special-k personal data processing transparency and compliance platform, 2021
Sabrina Kirrane, Javier D. Fernández, Piero Bonatti, Uros Milosevic, Axel Polleres, and Rigo Wenning · 2021
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Reproducibility as a mechanism for teaching fairness, accountability, confidentiality, and transparency in artificial intelligence, 2021
Ana Lucic, Maurits Bleeker, Sami Jullien, Samarth Bhargav, and Maarten de Rijke · 2021
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Traceability for trustworthy ai: A review of models and tools
Marçal Mora-Cantallops, Salvador Sánchez-Alonso, Elena García-Barriocanal, and Miguel-Angel Sicilia · 2021
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Manipulating and measuring model interpretability
Forough Poursabzi-Sangdeh, Daniel G. Goldstein, Jake Hofman, Jennifer Wortman Vaughan, and Hanna Wallach · 2021
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Modeling disclosive transparency in NLP application descriptions
Michael Saxon, Sharon Levy, Xinyi Wang, Alon Albalak, and William Yang Wang · 2021
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On the privacy risks of model explanations
Reza Shokri, Martin Strobel, and Yair Zick · 2021
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Microsoft is giving businesses access to openai’s powerful ai language model gpt-3, 2021
James Vincent · 2021
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Data analytics in a privacy-concerned world
Jaap Wieringa, PK Kannan, Xiao Ma, Thomas Reutterer, Hans Risselada, and Bernd Skiera · 2021
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Self-supervised knowledge assimilation for expert-layman text style transfer, 2021
Wenda Xu, Michael Saxon, Misha Sra, and William Yang Wang · 2021
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On sample based explanation methods for nlp: Faithfulness, efficiency and semantic evaluation
Wei Zhang, Ziming Huang, Yada Zhu, Guangnan Ye, Xiaodong Cui, and Fan Zhang · 2021
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Varieties of transparency: exploring agency within ai systems, 2022
Gloria Andrada, Robert W Clowes, and Paul R Smart · 2022
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Covid-19: Data collection and transparency among countries
Erwin Calgua · 2022
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User trust in recommendation systems: A comparison of content-based, collaborative and demographic filtering, 2022
M. Liao, S. S. Sundar, and J. B Walther · 2022
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Mitigating covertly unsafe text within natural language systems
Alex Mei, Anisha Kabir, Sharon Levy, Melanie Subbiah, Emily Allaway, John Judge, Desmond Patton, Bruce Bimber, Kathleen McKeown, and William Yang Wang · 2022
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Foveate, attribute, and rationalize: Towards safe and trustworthy ai
Alex Mei, Sharon Levy, and William Yang Wang · 2022
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