Fetching the paper…
Reading the bibliography…
Human-AI decision making is becoming increasingly ubiquitous, and explanations have been proposed to facilitate better Human-AI interactions.
Procedural Justice: A Psychological Analysis
J. W. Thibaut and L. Walker. 1975 · 1975
Earlier work this paper cites.
What Should Be Done with Equity Theory?
Gerald S. Leventhal. 1980 · 1980
Earlier work this paper cites.
Justice in the Workplace: From theory To Practice
Russell Cropanzano. 2012 · 2012
Earlier work this paper cites.
Fairness through Awareness. In Proceedings of the 3rd Innovations in Theoretical Computer Science Conference
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel. 2012 · 2012
Earlier work this paper cites.
Enterprise Data Analysis and Visualization: An Interview Study
S Kandel, A Paepcke, J M Hellerstein, and J Heer. 2012 · 2012
Earlier work this paper cites.
The Politics of Measurement and Action. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems
Kathleen H. Pine and Max Liboiron. 2015 · 2015
Earlier work this paper cites.
Equality of Opportunity in Supervised Learning. In Proceedings of the 30th International Conference on Neural Information Processing Systems
Moritz Hardt, Eric Price, and Nathan Srebro. 2016 · 2016
Earlier work this paper cites.
Weapons of math destruction: How big data increases inequality and threatens democracy
Cathy O’neil. 2016 · 2016
Earlier work this paper cites.
Situating methods in the magic of Big Data and AI
M. C. Elish and danah boyd. 2018 · 2017
Earlier work this paper cites.
Meaningful information and the right to explanation
Andrew D Selbst and Julia Powles. 2017 · 2017
Earlier work this paper cites.
’It’s Reducing a Human Being to a Percentage’; Perceptions of Justice in Algorithmic Decisions
Reuben Binns, Max Van Kleek, Michael Veale, Ulrik Lyngs, Jun Zhao, and Nigel Shadbolt. 2018 · 2018
Earlier work this paper cites.
Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency
Joy Buolamwini and Timnit Gebru. 2018 · 2018
Earlier work this paper cites.
The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standards
Sarah Holland, Ahmed Hosny, Sarah Newman, Joshua Joseph, and Kasia Chmielinski. 2018 · 2018
Earlier work this paper cites.
Model Cards for Model Reporting
Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru. 2018 · 2018
Earlier work this paper cites.
Trust in Data Science
Samir Passi and Steven J. Jackson. 2018 · 2018
Earlier work this paper cites.
Explaining Models: An Empirical Study of How Explanations Impact Fairness Judgment
Jonathan Dodge, Q. Vera Liao, Yunfeng Zhang, Rachel K. E. Bellamy, and Casey Dugan. 2019 · 2019
Cited alongside, same era.
Translating Principles into Practices of Digital Ethics: Five Risks of Being Unethical
Luciano Floridi. 2019 · 2019
Cited alongside, same era.
Improving Fairness in Machine Learning Systems. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
Kenneth Holstein, Jennifer Wortman Vaughan, Hal Daumé, Miro Dudik, and Hanna Wallach. 2019 · 2019
Cited alongside, same era.
How Data Science Workers Work with Data. 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
Cited alongside, same era.
Problem Formulation and Fairness. In Proceedings of the Conference on Fairness, Accountability, and Transparency
Samir Passi and Solon Barocas. 2019 · 2019
The Landscape and Gaps in Open Source Fairness Toolkits. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Michelle Seng Ah Lee and Jat Singh. 2021 · 2021
Later among the works it cites.
Conceptualising Contestability: Perspectives on Contesting Algorithmic Decisions
Henrietta Lyons, Eduardo Velloso, and Tim Miller. 2021 · 2021
Later among the works it cites.
Documenting Computer Vision Datasets. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency
Milagros Miceli, Tianling Yang, Laurens Naudts, Martin Schuessler, Diana Serbanescu, and Alex Hanna. 2021 · 2021
Later among the works it cites.
Where Responsible AI meets Reality
Bogdana Rakova, Jingying Yang, Henriette Cramer, and Rumman Chowdhury. 2021 · 2021
Later among the works it cites.
“Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Nithya Sambasivan, Shivani Kapania, Hannah Highfill, Diana Akrong, Praveen Paritosh, and Lora M Aroyo. 2021 · 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
FairPrep: Promoting Data to a First-Class Citizen in Studies on Fairness-Enhancing Interventions
Sebastian Schelter, Yuxuan He, Jatin Khilnani, and Julia Stoyanovich. 2019 · 2019
Cited alongside, same era.
Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
Michael A. Madaio, Luke Stark, Jennifer Wortman Vaughan, and Hanna Wallach. 2020 · 2020
Cited alongside, same era.
Making data science systems work
Samir Passi and Phoebe Sengers. 2020 · 2020
Cited alongside, same era.
Put Dialectics into the Machine: Protection against Automatic-decision-making through a Deeper Understanding of Contestability by Design
Claudio Sarra. 2020 · 2020
Cited alongside, same era.
Data-Centric Explanations: Explaining Training Data of Machine Learning Systems to Promote Transparency. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Ariful Islam Anik and Andrea Bunt. 2021 · 2021
Cited alongside, same era.
Managing bias and unfairness in data for decision support: a survey of machine learning and data engineering approaches to identify and mitigate bias and unfairness within data management and analytics systems
Agathe Balayn, Christoph Lofi, and Geert-Jan Houben. 2021 · 2021
Cited alongside, same era.
Proposal for regulation of the European parliament and of the council - Laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain Union legislative acts
European Commission. 2021 · 2021
Cited alongside, same era.
Later among the works it cites.
Ethical implications of fairness interventions: what might be hidden behind engineering choices?
Andrea Aler Tubella, Flavia Barsotti, Rüya Gökhan Koçer, and Julian Alfredo Mendez. 2022 · 2022
Later among the works it cites.
Contestable AI by Design: Towards a Framework
Kars Alfrink, Ianus Keller, Gerd Kortuem, and Neelke Doorn. 2022 · 2022
Later among the works it cites.
Model Positionality and Computational Reflexivity: Promoting Reflexivity in Data Science. In CHI Conference on Human Factors in Computing Systems
Scott Allen Cambo and Darren Gergle. 2022 · 2022
Later among the works it cites.
Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits. In 2022 ACM Conference on Fairness, Accountability, and Transparency
Wesley Hanwen Deng, Manish Nagireddy, Michelle Seng Ah Lee, Jatinder Singh, Zhiwei Steven Wu, Kenneth Holstein, and Haiyi Zhu. 2022 · 2022
Later among the works it cites.
Understanding Machine Learning Practitioners’ Data Documentation Perceptions, Needs, Challenges, and Desiderata
Amy Heger, Elizabeth B. Marquis, Mihaela Vorvoreanu, Hanna Wallach, and Jennifer Wortman Vaughan. 2022 · 2022
Later among the works it cites.
From critical technical practice to reflexive data science
Simon David Hirsbrunner, Michael Tebbe, and Claudia Müller-Birn. 2022 · 2022
Later among the works it cites.
Assessing the Fairness of AI Systems: AI Practitioners’ Processes, Challenges, and Needs for Support
Michael Madaio, Lisa Egede, Hariharan Subramonyam, Jennifer Wortman Vaughan, and Hanna Wallach. 2022 · 2022
Later among the works it cites.
Forgetting Practices in the Data Sciences. In CHI Conference on Human Factors in Computing Systems
Michael Muller and Angelika Strohmayer. 2022 · 2022
Later among the works it cites.
On the Relationship Between Explanations, Fairness Perceptions, and Decisions
Jakob Schoeffer, Maria De-Arteaga, and Niklas Kuehl. 2022 · 2022
Later among the works it cites.
When is Machine Learning Data Good?: Valuing in Public Health Datafication. In CHI Conference on Human Factors in Computing Systems
Divy Thakkar, Azra Ismail, Pratyush Kumar, Alex Hanna, Nithya Sambasivan, and Neha Kumar. 2022 · 2022
Later among the works it cites.