Table of Contents

• What to account for when accounting for algorithms: a systematic literature review on algorithmic accountabilityMaranke Wieringa. 1-18 [doi]
• Algorithmic realism: expanding the boundaries of algorithmic thoughtBen Green, Salomé Viljöen. 19-31 [doi]
• Algorithmic accountability in public administration: the GDPR paradoxSunny Seon Kang. 32 [doi]
• Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditingInioluwa Deborah Raji, Andrew Smart, Rebecca N. White, Margaret Mitchell, Timnit Gebru, Ben Hutchinson, Jamila Smith-Loud, Daniel Theron, Parker Barnes. 33-44 [doi]
• Toward situated interventions for algorithmic equity: lessons from the fieldMichael Katell, Meg Young, Dharma Dailey, Bernease Herman, Vivian Guetler, Aaron Tam, Corinne Binz, Daniella Raz, P. M. Krafft. 45-55 [doi]
• Explainability fact sheets: a framework for systematic assessment of explainable approachesKacper Sokol, Peter A. Flach. 56-67 [doi]
• Multi-layered explanations from algorithmic impact assessments in the GDPRMargot E. Kaminski, Gianclaudio Malgieri. 68-79 [doi]
• The hidden assumptions behind counterfactual explanations and principal reasonsSolon Barocas, Andrew D. Selbst, Manish Raghavan. 80-89 [doi]
• Why does my model fail?: contrastive local explanations for retail forecastingAna Lucic, Hinda Haned, Maarten de Rijke. 90-98 [doi]
• "The human body is a black box": supporting clinical decision-making with deep learningMark Sendak, Madeleine Clare Elish, Michael Gao, Joseph Futoma, William Ratliff, Marshall Nichols, Armando Bedoya, Suresh Balu, Cara O'Brien. 99-109 [doi]
• Assessing algorithmic fairness with unobserved protected class using data combinationNathan Kallus, Xiaojie Mao, Angela Zhou. 110 [doi]
• FlipTest: fairness testing via optimal transportEmily Black, Samuel Yeom, Matt Fredrikson. 111-121 [doi]
• Implications of AI (un-)fairness in higher education admissions: the effects of perceived AI (un-)fairness on exit, voice and organizational reputationFrank Marcinkowski, Kimon Kieslich, Christopher Starke, Marco Lünich. 122-130 [doi]
• Auditing radicalization pathways on YouTubeManoel Horta Ribeiro, Raphael Ottoni, Robert West 0001, Virgílio A. F. Almeida, Wagner Meira Jr.. 131-141 [doi]
• Case study: predictive fairness to reduce misdemeanor recidivism through social service interventionsKit T. Rodolfa, Erika Salomon, Lauren Haynes, Iván Higuera Mendieta, Jamie Larson, Rayid Ghani. 142-153 [doi]
• The concept of fairness in the GDPR: a linguistic and contextual interpretationGianclaudio Malgieri. 154-166 [doi]
• Studying up: reorienting the study of algorithmic fairness around issues of powerChelsea Barabas, Colin Doyle, J. B. Rubinovitz, Karthik Dinakar. 167-176 [doi]
• POTs: protective optimization technologiesBogdan Kulynych, Rebekah Overdorf, Carmela Troncoso, Seda F. Gürses. 177-188 [doi]
• Fair decision making using privacy-protected dataDavid Pujol, Ryan Mckenna, Satya Kuppam, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau. 189-199 [doi]
• Fairness warnings and fair-MAML: learning fairly with minimal dataDylan Slack, Sorelle A. Friedler, Emile Givental. 200-209 [doi]
• From ethics washing to ethics bashing: a view on tech ethics from within moral philosophyElettra Bietti. 210-219 [doi]
• Onward for the freedom of others: marching beyond the AI ethicsPetros Terzis. 220-229 [doi]
• Whose side are ethics codes on?: power, responsibility and the social goodAnne L. Washington, Rachel Kuo. 230-240 [doi]
• Algorithmic targeting of social policies: fairness, accuracy, and distributed governanceAlejandro Noriega-Campero, Bernardo Garcia-Bulle, Luis Fernando Cantu, Michiel A. Bakker, Luis Tejerina, Alex Pentland. 241-251 [doi]
• Roles for computing in social changeRediet Abebe, Solon Barocas, Jon M. Kleinberg, Karen Levy, Manish Raghavan, David G. Robinson. 252-260 [doi]
• Regulating transparency?: Facebook, Twitter and the german network enforcement actBen Wagner, Krisztina Rozgonyi, Marie-Therese Sekwenz, Jennifer Cobbe, Jatinder Singh. 261-271 [doi]
• The relationship between trust in AI and trustworthy machine learning technologiesEhsan Toreini, Mhairi Aitken, Kovila P. L. Coopamootoo, Karen Elliott, Carlos Gonzalez Zelaya, Aad van Moorsel. 272-283 [doi]
• The philosophical basis of algorithmic recourseSuresh Venkatasubramanian, Mark Alfano. 284-293 [doi]
• Value-laden disciplinary shifts in machine learningRavit Dotan, Smitha Milli. 294 [doi]
• Effect of confidence and explanation on accuracy and trust calibration in AI-assisted decision makingYunfeng Zhang, Q. Vera Liao, Rachel K. E. Bellamy. 295-305 [doi]
• Lessons from archives: strategies for collecting sociocultural data in machine learningEun Seo Jo, Timnit Gebru. 306-316 [doi]
• Data in New Delhi's predictive policing systemVidushi Marda, Shivangi Narayan. 317-324 [doi]
• Garbage in, garbage out?: do machine learning application papers in social computing report where human-labeled training data comes from?R. Stuart Geiger, Kevin Yu, Yanlai Yang, Mindy Dai, Jie Qiu, Rebekah Tang, Jenny Huang. 325-336 [doi]
• Bidding strategies with gender nondiscrimination constraints for online ad auctionsMilad Nasr, Michael Carl Tschantz. 337-347 [doi]
• Multi-category fairness in sponsored search auctionsChristina Ilvento, Meena Jagadeesan, Shuchi Chawla 0001. 348-358 [doi]
• Reducing sentiment polarity for demographic attributes in word embeddings using adversarial learningChris Sweeney, Maryam Najafian. 359-368 [doi]
• Interventions for ranking in the presence of implicit biasL. Elisa Celis, Anay Mehrotra, Nisheeth K. Vishnoi. 369-380 [doi]
• The disparate equilibria of algorithmic decision making when individuals invest rationallyLydia T. Liu, Ashia Wilson, Nika Haghtalab, Adam Tauman Kalai, Christian Borgs, Jennifer T. Chayes. 381-391 [doi]
• An empirical study on the perceived fairness of realistic, imperfect machine learning modelsGalen Harrison, Julia Hanson, Christine Jacinto, Julio Ramirez, Blase Ur. 392-402 [doi]
• Artificial mental phenomena: psychophysics as a framework to detect perception biases in AI modelsLizhen Liang, Daniel E. Acuña. 403-412 [doi]
• The social lives of generative adversarial networksMichael Castelle. 413 [doi]
• Towards a more representative politics in the ethics of computer scienceJared Moore. 414-424 [doi]
• Integrating FATE/critical data studies into data science curricula: where are we going and how do we get there?Jo Bates, David Cameron, Alessandro Checco, Paul D. Clough, Frank Hopfgartner, Suvodeep Mazumdar, Laura Sbaffi, Peter Stordy, Antonio de la Vega de León. 425-435 [doi]
• Recommendations and user agency: the reachability of collaboratively-filtered informationSarah Dean, Sarah Rich, Benjamin Recht. 436-445 [doi]
• Bias in word embeddingsOrestis Papakyriakopoulos, Simon Hegelich, Juan Carlos Medina Serrano, Fabienne Marco. 446-457 [doi]
• What does it mean to 'solve' the problem of discrimination in hiring?: social, technical and legal perspectives from the UK on automated hiring systemsJavier Sánchez-Monedero, Lina Dencik, Lilian Edwards. 458-468 [doi]
• Mitigating bias in algorithmic hiring: evaluating claims and practicesManish Raghavan, Solon Barocas, Jon M. Kleinberg, Karen Levy. 469-481 [doi]
• The impact of overbooking on a pre-trial risk assessment toolKristian Lum, Chesa Boudin, Megan Price. 482-491 [doi]
• Awareness in practice: tensions in access to sensitive attribute data for antidiscriminationMiranda Bogen, Aaron Rieke, Shazeda Ahmed. 492-500 [doi]
• Towards a critical race methodology in algorithmic fairnessAlex Hanna, Emily Denton, Andrew Smart, Jamila Smith-Loud. 501-512 [doi]
• What's sex got to do with machine learning?Lily Hu, Issa Kohler-Hausmann. 513 [doi]
• On the apparent conflict between individual and group fairnessReuben Binns. 514-524 [doi]
• Fairness is not static: deeper understanding of long term fairness via simulation studiesAlexander D'Amour, Hansa Srinivasan, James Atwood, Pallavi Baljekar, D. Sculley, Yoni Halpern. 525-534 [doi]
• Fair classification and social welfareLily Hu, Yiling Chen. 535-545 [doi]
• Preference-informed fairnessMichael P. Kim, Aleksandra Korolova, Guy N. Rothblum, Gal Yona. 546 [doi]
• Towards fairer datasets: filtering and balancing the distribution of the people subtree in the ImageNet hierarchyKaiyu Yang, Klint Qinami, Li Fei-Fei 0001, Jia Deng, Olga Russakovsky. 547-558 [doi]
• The case for voter-centered audits of search engines during political electionsEni Mustafaraj, Emma Lurie, Claire Devine. 559-569 [doi]
• Whose tweets are surveilled for the police: an audit of a social-media monitoring tool via log filesGlencora Borradaile, Brett Burkhardt, Alexandria LeClerc. 570-580 [doi]
• Dirichlet uncertainty wrappers for actionable algorithm accuracy accountability and auditabilityJosé Mena Roldán, Oriol Pujol Vila, Jordi Vitrià Marca. 581 [doi]
• Counterfactual risk assessments, evaluation, and fairnessAmanda Coston, Alan Mishler, Edward H. Kennedy, Alexandra Chouldechova. 582-593 [doi]
• The false promise of risk assessments: epistemic reform and the limits of fairnessBen Green. 594-606 [doi]
• Explaining machine learning classifiers through diverse counterfactual explanationsRamaravind Kommiya Mothilal, Amit Sharma, Chenhao Tan. 607-617 [doi]
• Model agnostic interpretability of rankers via intent modellingJaspreet Singh, Avishek Anand. 618-628 [doi]
• Doctor XAI: an ontology-based approach to black-box sequential data classification explanationsCecilia Panigutti, Alan Perotti, Dino Pedreschi. 629-639 [doi]
• Robustness in machine learning explanations: does it matter?Leif Hancox-Li. 640-647 [doi]
• Explainable machine learning in deploymentUmang Bhatt, Alice Xiang, Shubham Sharma, Adrian Weller, Ankur Taly, Yunhan Jia, Joydeep Ghosh, Ruchir Puri, José M. F. Moura, Peter Eckersley. 648-657 [doi]
• Fairness and utilization in allocating resources with uncertain demandKate Donahue, Jon M. Kleinberg. 658-668 [doi]
• The effects of competition and regulation on error inequality in data-driven marketsHadi Elzayn, Benjamin Fish. 669-679 [doi]
• Measuring justice in machine learningAlan Lundgard. 680 [doi]
• Algorithmically encoded identities: reframing human classificationDylan Baker, Alex Hanna, Emily Denton. 681 [doi]
• Bridging the gap from AI ethics research to practiceKathy Baxter, Yoav Schlesinger, Sarah Aerni, Lewis Baker, Julie Dawson, Krishnaram Kenthapadi, Isabel M. Kloumann, Hanna M. Wallach. 682 [doi]
• Burn, dream and reboot!: speculating backwards for the missing archive on non-coercive computingHelen Pritchard, Eric Snodgrass, Romi Ron Morrison, Loren Britton, Joana Moll. 683 [doi]
• Centering disability perspectives in algorithmic fairness, accountability, & transparencyAlexandra Reeve Givens, Meredith Ringel Morris. 684 [doi]
• Creating community-based tech policy: case studies, lessons learned, and what technologists and communities can do togetherHannah Sassaman, Jennifer Lee, Jenessa Irvine, Shankar Narayan. 685 [doi]
• CtrlZ.AI zine fair: critical perspectivesAlex Hanna, Emily Denton. 686 [doi]
• Deconstructing FAT: using memories to collectively explore implicit assumptions, values and context in practices of debiasing and discrimination-awarenessDoris Allhutter, Bettina Berendt. 687 [doi]
• Ethics on the ground: from principles to practiceMarguerite Barry, Aphra Kerr, Oliver Smith. 688 [doi]
• Where do algorithmic accountability and explainability frameworks take us in the real world?: from theory to practiceKatarzyna Szymielewicz, Anna Bacciarelli, Fanny Hidvegi, Agata Foryciarz, Soizic Pénicaud, Matthias Spielkamp. 689 [doi]
• Fairness, accountability, transparency in AI at scale: lessons from national programsMuhammad Aurangzeb Ahmad, Ankur Teredesai, Carly Eckert. 690 [doi]
• Hardwiring discriminatory police practices: the implications of data-driven technological policing on minority (ethnic and religious) people and communitiesPatrick Williams, Eric Kind. 691 [doi]
• Lost in translation: an interactive workshop mapping interdisciplinary translations for epistemic justiceEvelyn Wan, Aviva de Groot, Shazade Jameson, Mara Paun, Phillip Lücking, Goda Klumbyte, Danny Lämmerhirt. 692 [doi]
• Manifesting the sociotechnical: experimenting with methods for social context and social justiceEzra Goss, Lily Hu, Manuel Sabin, Stephanie Teeple. 693 [doi]
• When not to design, build, or deploySolon Barocas, Asia J. Biega, Benjamin Fish, Jedrzej Niklas, Luke Stark. 695 [doi]
• AI explainability 360: hands-on tutorialVijay 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. 696 [doi]
• Assessing the intersection of organizational structure and FAT* efforts within industry: implications tutorialBogdana Rakova, Rumman Chowdhury, Jingying Yang. 697 [doi]
• Can an algorithmic system be a 'friend' to a police officer's discretion?: ACM FAT 2020 translation tutorialMarion Oswald, David Powell. 698 [doi]
• Explainable AI in industry: practical challenges and lessons learned: implications tutorialKrishna Gade, Sahin Cem Geyik, Krishnaram Kenthapadi, Varun Mithal, Ankur Taly. 699 [doi]
• Experimentation with fairness-aware recommendation using librec-auto: hands-on tutorialRobin D. Burke, Masoud Mansoury, Nasim Sonboli. 700 [doi]
• From the total survey error framework to an error framework for digital traces of humans: translation tutorialIndira Sen, Fabian Flöck, Katrin Weller, Bernd Weiss, Claudia Wagner. 701 [doi]
• Leap of FATE: human rights as a complementary framework for AI policy and practiceCorinne Cath, Mark Latonero, Vidushi Marda, Roya Pakzad. 702 [doi]
• Policy 101: an introduction to public policymaking in the EU and USNatasha Duarte, Stan Adams. 703 [doi]
• Positionality-aware machine learning: translation tutorialChristine Kaeser-Chen, Elizabeth Dubois, Friederike Schuur, Emanuel Moss. 704 [doi]
• Probing ML models for fairness with the what-if tool and SHAP: hands-on tutorialJames Wexler, Mahima Pushkarna, Sara Robinson, Tolga Bolukbasi, Andrew Zaldivar. 705 [doi]
• The meaning and measurement of bias: lessons from natural language processingAbigail Z. Jacobs, Su Lin Blodgett, Solon Barocas, Hal Daumé III, Hanna M. Wallach. 706 [doi]
• 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 tutorialMaya Indira Ganesh, Francien Dechesne, Zeerak Waseem. 707 [doi]