FAT*

Keyphrases

Publications

2020

Muhammad Aurangzeb Ahmad, Ankur Teredesai, Carly Eckert
Fairness, accountability, transparency in AI at scale: lessons from national programs. FAT* (2020)
Katarzyna Szymielewicz, Anna Bacciarelli, Fanny Hidvegi, Agata Foryciarz, Soizic Pénicaud, Matthias Spielkamp
Where do algorithmic accountability and explainability frameworks take us in the real world?: from theory to practice. FAT* (2020)
Maranke Wieringa
What to account for when accounting for algorithms: a systematic literature review on algorithmic accountability. FAT* (2020)
L. Elisa Celis, Anay Mehrotra, Nisheeth K. Vishnoi
Interventions for ranking in the presence of implicit bias. FAT* (2020)
Dylan Slack, Sorelle A. Friedler, Emile Givental
Fairness warnings and fair-MAML: learning fairly with minimal data. FAT* (2020)
Manoel Horta Ribeiro, Raphael Ottoni, Robert West, Virgílio A. F. Almeida, Wagner Meira Jr.
Auditing radicalization pathways on YouTube. FAT* (2020)
Kaiyu Yang, Klint Qinami, Li Fei-Fei, Jia Deng, Olga Russakovsky
Towards fairer datasets: filtering and balancing the distribution of the people subtree in the ImageNet hierarchy. FAT* (2020)
Ana Lucic, Hinda Haned, Maarten de Rijke
Why does my model fail?: contrastive local explanations for retail forecasting. FAT* (2020)
Lily Hu, Issa Kohler-Hausmann
What's sex got to do with machine learning? FAT* (2020)
Alejandro Noriega-Campero, Bernardo Garcia-Bulle, Luis Fernando Cantu, Michiel A. Bakker, Luis Tejerina, Alex Pentland
Algorithmic targeting of social policies: fairness, accuracy, and distributed governance. FAT* (2020)
Maya Indira Ganesh, Francien Dechesne, Zeerak Waseem
Two computer scientists and a cultural scientist get hit by a driver-less car: a method for situating knowledge in the cross-disciplinary study of F-A-T in machine learning: translation tutorial. FAT* (2020)
Yunfeng Zhang, Q. Vera Liao, Rachel K. E. Bellamy
Effect of confidence and explanation on accuracy and trust calibration in AI-assisted decision making. FAT* (2020)
Elettra Bietti
From ethics washing to ethics bashing: a view on tech ethics from within moral philosophy. FAT* (2020)
Lizhen Liang, Daniel E. Acuna
Artificial mental phenomena: psychophysics as a framework to detect perception biases in AI models. FAT* (2020)
Anne L. Washington, Rachel S. Kuo
Whose side are ethics codes on?: power, responsibility and the social good. FAT* (2020)
Ben Green
The false promise of risk assessments: epistemic reform and the limits of fairness. FAT* (2020)
Emily Black, Samuel Yeom, Matt Fredrikson
FlipTest: fairness testing via optimal transport. FAT* (2020)
Galen Harrison, Julia Hanson, Christine Jacinto, Julio Ramirez, Blase Ur
An empirical study on the perceived fairness of realistic, imperfect machine learning models. FAT* (2020)
Solon Barocas, Andrew D. Selbst, Manish Raghavan
The hidden assumptions behind counterfactual explanations and principal reasons. FAT* (2020)
Glencora Borradaile, Brett Burkhardt, Alexandria LeClerc
Whose tweets are surveilled for the police: an audit of a social-media monitoring tool via log files. FAT* (2020)
Michael A. Katell, Meg Young, Dharma Dailey, Bernease Herman, Vivian Guetler, Aaron Tam, Corinne Bintz, Daniella Raz, P. M. Krafft
Toward situated interventions for algorithmic equity: lessons from the field. FAT* (2020)
Suresh Venkatasubramanian, Mark Alfano
The philosophical basis of algorithmic recourse. FAT* (2020)
Rediet Abebe, Solon Barocas, Jon M. Kleinberg, Karen Levy, Manish Raghavan, David G. Robinson
Roles for computing in social change. FAT* (2020)
Gianclaudio Malgieri
The concept of fairness in the GDPR: a linguistic and contextual interpretation. FAT* (2020)
Ben Wagner, Krisztina Rozgonyi, Marie-Therese Sekwenz, Jennifer Cobbe, Jatinder Singh
Regulating transparency?: Facebook, Twitter and the german network enforcement act. FAT* (2020)
Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Q. Vera Liao, Ronny Luss, Aleksandra Mojsilovic, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang
AI explainability 360: hands-on tutorial. FAT* (2020)
Ben Green, Salomé Viljöen
Algorithmic realism: expanding the boundaries of algorithmic thought. FAT* (2020)
Kacper Sokol, Peter A. Flach
Explainability fact sheets: a framework for systematic assessment of explainable approaches. FAT* (2020)
Michael P. Kim, Aleksandra Korolova, Guy N. Rothblum, Gal Yona
Preference-informed fairness. FAT* (2020)
Inioluwa Deborah Raji, Andrew Smart, Rebecca N. White, Margaret Mitchell, Timnit Gebru, Ben Hutchinson, Jamila Smith-Loud, Daniel Theron, Parker Barnes
Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing. FAT* (2020)
Eun Seo Jo, Timnit Gebru
Lessons from archives: strategies for collecting sociocultural data in machine learning. FAT* (2020)
Ehsan Toreini, Mhairi Aitken, Kovila P. L. Coopamootoo, Karen Elliott, Carlos Gonzalez Zelaya, Aad van Moorsel
The relationship between trust in AI and trustworthy machine learning technologies. FAT* (2020)
Manish Raghavan, Solon Barocas, Jon M. Kleinberg, Karen Levy
Mitigating bias in algorithmic hiring: evaluating claims and practices. FAT* (2020)
Chelsea Barabas, Colin Doyle, J. B. Rubinovitz, Karthik Dinakar
Studying up: reorienting the study of algorithmic fairness around issues of power. FAT* (2020)
Kate Donahue, Jon M. Kleinberg
Fairness and utilization in allocating resources with uncertain demand. FAT* (2020)
Petros Terzis
Onward for the freedom of others: marching beyond the AI ethics. FAT* (2020)
Alexandra Reeve Givens, Meredith Ringel Morris
Centering disability perspectives in algorithmic fairness, accountability, & transparency. FAT* (2020)
Kit T. Rodolfa, Erika Salomon, Lauren Haynes, Iván Higuera Mendieta, Jamie Larson, Rayid Ghani
Case study: predictive fairness to reduce misdemeanor recidivism through social service interventions. FAT* (2020)
Jared Moore
Towards a more representative politics in the ethics of computer science. FAT* (2020)
Ravit Dotan, Smitha Milli
Value-laden disciplinary shifts in machine learning. FAT* (2020)
Helen Pritchard, Eric Snodgrass, Romi Ron Morrison, Loren Britton, Joana Moll
Burn, dream and reboot!: speculating backwards for the missing archive on non-coercive computing. FAT* (2020)
Mark P. Sendak, Madeleine Clare Elish, Michael Gao, Joseph Futoma, William Ratliff, Marshall Nichols, Armando Bedoya, Suresh Balu, Cara O'Brien
"The human body is a black box": supporting clinical decision-making with deep learning. FAT* (2020)
Indira Sen, Fabian Flöck, Katrin Weller, Bernd Weiss, Claudia Wagner
From the total survey error framework to an error framework for digital traces of humans: translation tutorial. FAT* (2020)
Margot E. Kaminski, Gianclaudio Malgieri
Multi-layered explanations from algorithmic impact assessments in the GDPR. FAT* (2020)
Bogdan Kulynych, Rebekah Overdorf, Carmela Troncoso, Seda F. Gürses
POTs: protective optimization technologies. FAT* (2020)
Nathan Kallus, Xiaojie Mao, Angela Zhou
Assessing algorithmic fairness with unobserved protected class using data combination. FAT* (2020)
Sunny Seon Kang
Algorithmic accountability in public administration: the GDPR paradox. FAT* (2020)
David Pujol, Ryan McKenna, Satya Kuppam, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau
Fair decision making using privacy-protected data. FAT* (2020)
FAT* '20: Conference on Fairness, Accountability, and Transparency, Barcelona, Spain, January 27-30, 2020 FAT* (2020)
Frank Marcinkowski, Kimon Kieslich, Christopher Starke, Marco Lünich
Implications of AI (un-)fairness in higher education admissions: the effects of perceived AI (un-)fairness on exit, voice and organizational reputation. FAT* (2020)