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ORCIDFAT* 2020: Barcelona, Spain
- Mireille Hildebrandt, Carlos Castillo, L. Elisa Celis, Salvatore Ruggieri, Linnet Taylor, Gabriela Zanfir-Fortuna:
FAT* '20: Conference on Fairness, Accountability, and Transparency, Barcelona, Spain, January 27-30, 2020. ACM 2020, ISBN 978-1-4503-6936-7 - Maranke Wieringa:
What to account for when accounting for algorithms: a systematic literature review on algorithmic accountability. 1-18 - Ben Green, Salomé Viljöen:
Algorithmic realism: expanding the boundaries of algorithmic thought. 19-31 - Sunny Seon Kang:
Algorithmic accountability in public administration: the GDPR paradox. 32 - 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. 33-44 - 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. 45-55 - Kacper Sokol, Peter A. Flach:
Explainability fact sheets: a framework for systematic assessment of explainable approaches. 56-67 - Margot E. Kaminski, Gianclaudio Malgieri:
Multi-layered explanations from algorithmic impact assessments in the GDPR. 68-79 - Solon Barocas, Andrew D. Selbst, Manish Raghavan:
The hidden assumptions behind counterfactual explanations and principal reasons. 80-89 - Ana Lucic, Hinda Haned, Maarten de Rijke:
Why does my model fail?: contrastive local explanations for retail forecasting. 90-98 - 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. 99-109 - Nathan Kallus, Xiaojie Mao, Angela Zhou:
Assessing algorithmic fairness with unobserved protected class using data combination. 110 - Emily Black, Samuel Yeom, Matt Fredrikson:
FlipTest: fairness testing via optimal transport. 111-121 - 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. 122-130 - Manoel Horta Ribeiro, Raphael Ottoni, Robert West, Virgílio A. F. Almeida, Wagner Meira Jr.:
Auditing radicalization pathways on YouTube. 131-141 - 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. 142-153 - Gianclaudio Malgieri:
The concept of fairness in the GDPR: a linguistic and contextual interpretation. 154-166 - Chelsea Barabas, Colin Doyle, J. B. Rubinovitz, Karthik Dinakar:
Studying up: reorienting the study of algorithmic fairness around issues of power. 167-176 - Bogdan Kulynych, Rebekah Overdorf, Carmela Troncoso, Seda F. Gürses:
POTs: protective optimization technologies. 177-188 - David Pujol, Ryan McKenna, Satya Kuppam, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau:
Fair decision making using privacy-protected data. 189-199 - Dylan Slack, Sorelle A. Friedler, Emile Givental:
Fairness warnings and fair-MAML: learning fairly with minimal data. 200-209 - Elettra Bietti:
From ethics washing to ethics bashing: a view on tech ethics from within moral philosophy. 210-219 - Petros Terzis:
Onward for the freedom of others: marching beyond the AI ethics. 220-229 - Anne L. Washington, Rachel Kuo:
Whose side are ethics codes on?: power, responsibility and the social good. 230-240 - 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. 241-251 - Rediet Abebe, Solon Barocas, Jon M. Kleinberg, Karen Levy, Manish Raghavan, David G. Robinson:
Roles for computing in social change. 252-260 - Ben Wagner, Krisztina Rozgonyi, Marie-Therese Sekwenz, Jennifer Cobbe, Jatinder Singh:
Regulating transparency?: Facebook, Twitter and the german network enforcement act. 261-271 - 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. 272-283 - Suresh Venkatasubramanian, Mark Alfano:
The philosophical basis of algorithmic recourse. 284-293 - Ravit Dotan, Smitha Milli:
Value-laden disciplinary shifts in machine learning. 294 - Yunfeng Zhang, Q. Vera Liao, Rachel K. E. Bellamy:
Effect of confidence and explanation on accuracy and trust calibration in AI-assisted decision making. 295-305 - Eun Seo Jo, Timnit Gebru:
Lessons from archives: strategies for collecting sociocultural data in machine learning. 306-316 - Vidushi Marda, Shivangi Narayan:
Data in New Delhi's predictive policing system. 317-324 - R. Stuart Geiger, Kevin Yu, Yanlai Yang, Mindy Dai, Jie Qiu, Rebekah Tang, Jenny Huang:
Garbage in, garbage out?: do machine learning application papers in social computing report where human-labeled training data comes from? 325-336 - Milad Nasr, Michael Carl Tschantz:
Bidding strategies with gender nondiscrimination constraints for online ad auctions. 337-347 - Christina Ilvento, Meena Jagadeesan, Shuchi Chawla:
Multi-category fairness in sponsored search auctions. 348-358 - Chris Sweeney, Maryam Najafian:
Reducing sentiment polarity for demographic attributes in word embeddings using adversarial learning. 359-368 - L. Elisa Celis, Anay Mehrotra, Nisheeth K. Vishnoi:
Interventions for ranking in the presence of implicit bias. 369-380 - Lydia T. Liu, Ashia Wilson, Nika Haghtalab, Adam Tauman Kalai, Christian Borgs, Jennifer T. Chayes:
The disparate equilibria of algorithmic decision making when individuals invest rationally. 381-391 - Galen Harrison, Julia Hanson, Christine Jacinto, Julio Ramirez, Blase Ur:
An empirical study on the perceived fairness of realistic, imperfect machine learning models. 392-402 - Lizhen Liang, Daniel E. Acuna:
Artificial mental phenomena: psychophysics as a framework to detect perception biases in AI models. 403-412 - Michael Castelle:
The social lives of generative adversarial networks. 413 - Jared Moore:
Towards a more representative politics in the ethics of computer science. 414-424 - Jo Bates, David Cameron, Alessandro Checco, Paul D. Clough, Frank Hopfgartner, Suvodeep Mazumdar, Laura Sbaffi, Peter Stordy, Antonio de la Vega de León:
Integrating FATE/critical data studies into data science curricula: where are we going and how do we get there? 425-435 - Sarah Dean, Sarah Rich, Benjamin Recht:
Recommendations and user agency: the reachability of collaboratively-filtered information. 436-445 - Orestis Papakyriakopoulos, Simon Hegelich, Juan Carlos Medina Serrano, Fabienne Marco:
Bias in word embeddings. 446-457 - Javier Sánchez-Monedero, Lina Dencik, Lilian Edwards:
What does it mean to 'solve' the problem of discrimination in hiring?: social, technical and legal perspectives from the UK on automated hiring systems. 458-468 - Manish Raghavan, Solon Barocas, Jon M. Kleinberg, Karen Levy:
Mitigating bias in algorithmic hiring: evaluating claims and practices. 469-481 - Kristian Lum, Chesa Boudin, Megan Price:
The impact of overbooking on a pre-trial risk assessment tool. 482-491 - Miranda Bogen, Aaron Rieke, Shazeda Ahmed:
Awareness in practice: tensions in access to sensitive attribute data for antidiscrimination. 492-500 - Alex Hanna, Emily Denton, Andrew Smart, Jamila Smith-Loud:
Towards a critical race methodology in algorithmic fairness. 501-512 - Lily Hu, Issa Kohler-Hausmann:
What's sex got to do with machine learning? 513 - Reuben Binns:
On the apparent conflict between individual and group fairness. 514-524 - Alexander D'Amour, Hansa Srinivasan, James Atwood, Pallavi Baljekar, D. Sculley, Yoni Halpern:
Fairness is not static: deeper understanding of long term fairness via simulation studies. 525-534 - Lily Hu, Yiling Chen:
Fair classification and social welfare. 535-545 - Michael P. Kim, Aleksandra Korolova, Guy N. Rothblum, Gal Yona:
Preference-informed fairness. 546 - 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. 547-558 - Eni Mustafaraj, Emma Lurie, Claire Devine:
The case for voter-centered audits of search engines during political elections. 559-569 - Glencora Borradaile, Brett Burkhardt, Alexandria LeClerc:
Whose tweets are surveilled for the police: an audit of a social-media monitoring tool via log files. 570-580 - José Mena Roldán, Oriol Pujol Vila, Jordi Vitrià Marca:
Dirichlet uncertainty wrappers for actionable algorithm accuracy accountability and auditability. 581 - Amanda Coston, Alan Mishler, Edward H. Kennedy, Alexandra Chouldechova:
Counterfactual risk assessments, evaluation, and fairness. 582-593 - Ben Green:
The false promise of risk assessments: epistemic reform and the limits of fairness. 594-606 - Ramaravind Kommiya Mothilal, Amit Sharma, Chenhao Tan:
Explaining machine learning classifiers through diverse counterfactual explanations. 607-617 - Jaspreet Singh, Avishek Anand:
Model agnostic interpretability of rankers via intent modelling. 618-628 - Cecilia Panigutti, Alan Perotti, Dino Pedreschi:
Doctor XAI: an ontology-based approach to black-box sequential data classification explanations. 629-639 - Leif Hancox-Li:
Robustness in machine learning explanations: does it matter? 640-647 - Umang Bhatt, Alice Xiang, Shubham Sharma, Adrian Weller, Ankur Taly, Yunhan Jia, Joydeep Ghosh, Ruchir Puri, José M. F. Moura, Peter Eckersley:
Explainable machine learning in deployment. 648-657 - Kate Donahue, Jon M. Kleinberg:
Fairness and utilization in allocating resources with uncertain demand. 658-668 - Hadi Elzayn, Benjamin Fish:
The effects of competition and regulation on error inequality in data-driven markets. 669-679 - Alan Lundgard:
Measuring justice in machine learning. 680 - Dylan K. Baker, Alex Hanna, Emily Denton:
Algorithmically encoded identities: reframing human classification. 681 - Kathy Baxter, Yoav Schlesinger, Sarah Aerni, Lewis J. Baker, Julie Dawson, Krishnaram Kenthapadi, Isabel M. Kloumann, Hanna M. Wallach:
Bridging the gap from AI ethics research to practice. 682 - 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. 683 - Alexandra Reeve Givens, Meredith Ringel Morris:
Centering disability perspectives in algorithmic fairness, accountability, & transparency. 684 - Hannah Sassaman, Jennifer E. Lee, Jenessa Irvine, Shankar Narayan:
Creating community-based tech policy: case studies, lessons learned, and what technologists and communities can do together. 685 - Alex Hanna, Emily Denton:
CtrlZ.AI zine fair: critical perspectives. 686 - Doris Allhutter, Bettina Berendt:
Deconstructing FAT: using memories to collectively explore implicit assumptions, values and context in practices of debiasing and discrimination-awareness. 687 - Marguerite Barry, Aphra Kerr, Oliver Smith:
Ethics on the ground: from principles to practice. 688 - 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. 689 - Muhammad Aurangzeb Ahmad, Ankur Teredesai, Carly Eckert:
Fairness, accountability, transparency in AI at scale: lessons from national programs. 690 - Patrick Williams, Eric Kind:
Hardwiring discriminatory police practices: the implications of data-driven technological policing on minority (ethnic and religious) people and communities. 691 - Evelyn Wan, Aviva de Groot, Shazade Jameson, Mara Paun, Phillip Lücking, Goda Klumbyte, Danny Lämmerhirt:
Lost in translation: an interactive workshop mapping interdisciplinary translations for epistemic justice. 692 - Ezra Goss, Lily Hu, Manuel Sabin, Stephanie Teeple:
Manifesting the sociotechnical: experimenting with methods for social context and social justice. 693 - Solon Barocas, Asia J. Biega, Benjamin Fish, Jedrzej Niklas, Luke Stark:
When not to design, build, or deploy. 695 - 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. 696 - Bogdana Rakova, Rumman Chowdhury, Jingying Yang:
Assessing the intersection of organizational structure and FAT* efforts within industry: implications tutorial. 697 - Marion Oswald, David Powell:
Can an algorithmic system be a 'friend' to a police officer's discretion?: ACM FAT 2020 translation tutorial. 698 - Krishna Gade, Sahin Cem Geyik, Krishnaram Kenthapadi, Varun Mithal, Ankur Taly:
Explainable AI in industry: practical challenges and lessons learned: implications tutorial. 699 - Robin D. Burke, Masoud Mansoury, Nasim Sonboli:
Experimentation with fairness-aware recommendation using librec-auto: hands-on tutorial. 700 - 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. 701 - Corinne Cath, Mark Latonero, Vidushi Marda, Roya Pakzad:
Leap of FATE: human rights as a complementary framework for AI policy and practice. 702 - Natasha Duarte, Stan Adams:
Policy 101: an introduction to public policymaking in the EU and US. 703 - Christine Kaeser-Chen, Elizabeth Dubois, Friederike Schuur, Emanuel Moss:
Positionality-aware machine learning: translation tutorial. 704 - James Wexler, Mahima Pushkarna, Sara Robinson, Tolga Bolukbasi, Andrew Zaldivar:
Probing ML models for fairness with the what-if tool and SHAP: hands-on tutorial. 705 - Abigail Z. Jacobs, Su Lin Blodgett, Solon Barocas, Hal Daumé III, Hanna M. Wallach:
The meaning and measurement of bias: lessons from natural language processing. 706 - 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. 707
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last updated on 2025-03-21 23:49 CET by the dblp team
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