Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/3442188.3445885acmconferencesArticle/Chapter ViewAbstractPublication PagesfacctConference Proceedingsconference-collections
research-article
Public Access

Data Leverage: A Framework for Empowering the Public in its Relationship with Technology Companies

Published: 01 March 2021 Publication History

Abstract

Many powerful computing technologies rely on implicit and explicit data contributions from the public. This dependency suggests a potential source of leverage for the public in its relationship with technology companies: by reducing, stopping, redirecting, or otherwise manipulating data contributions, the public can reduce the effectiveness of many lucrative technologies. In this paper, we synthesize emerging research that seeks to better understand and help people action this data leverage. Drawing on prior work in areas including machine learning, human-computer interaction, and fairness and accountability in computing, we present a framework for understanding data leverage that highlights new opportunities to change technology company behavior related to privacy, economic inequality, content moderation and other areas of societal concern. Our framework also points towards ways that policymakers can bolster data leverage as a means of changing the balance of power between the public and tech companies.

References

[1]
2018. Art. 20 GDPR - Right to data portability | General Data Protection Regulation (GDPR). https://gdpr-info.eu/art-20-gdpr
[2]
2020. 18 U.S. Code § 1030 - Fraud and related activity in connection with computers. https://www.law.cornell.edu/uscode/text/18/1030 [Online; accessed 7. Oct. 2020].
[3]
2020. Tech Workers Coalition. https://techworkerscoalition.org [Online; accessed 6. Oct. 2020].
[4]
2020. Wikipedia:Academic studies of Wikipedia - Wikipedia. https://en.wikipedia.org/w/index.php?title=Wikipedia:Academic_studies_of_Wikipedia&oldid=971074694 [Online; accessed 29. Sep. 2020].
[5]
Rediet Abebe, Solon Barocas, Jon Kleinberg, Karen Levy, Manish Raghavan, and David G Robinson. 2020. Roles for computing in social change. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. 252--260.
[6]
Daron Acemoglu, Ali Makhdoumi, Azarakhsh Malekian, and Asuman Ozdaglar. 2019. Too much data: Prices and inefficiencies in data markets. Technical Report. National Bureau of Economic Research.
[7]
Kendra Albert, Jon Penney, Bruce Schneier, and Ram Shankar Siva Kumar. 2020. Politics of Adversarial Machine Learning. In Towards Trustworthy ML: Rethinking Security and Privacy for ML Workshop, Eighth International Conference on Learning Representations (ICLR).
[8]
Imanol Arrieta Ibarra, Leonard Goff, Diego Jiménez Hernández, Jaron Lanier, and E Weyl. 2018. Should We Treat Data as Labor? Moving Beyond 'Free'. American Economic Association Papers & Proceedings 1, 1 (2018).
[9]
Stefan Baack. 2015. Datafication and empowerment: How the open data movement re-articulates notions of democracy, participation, and journalism. Big Data & Society 2, 2 (2015), 2053951715594634. https://doi.org/10.1177/2053951715594634 arXiv:https://doi.org/10.1177/2053951715594634
[10]
Marco Barreno, Blaine Nelson, Russell Sears, Anthony D Joseph, and J Doug Tygar. 2006. Can machine learning be secure?. In Proceedings of the 2006 ACM Symposium on Information, computer and communications security. 16--25.
[11]
Ames Morgan G. Brubaker Jed R. Burrell Jenna Dourish Paul Baumer, Eric PS. 2014. Refusing, Limiting, Departing: Why We Should Study Technology Non-Use. In CHI EA '14: CHI '14 Extended Abstracts on Human Factors in Computing Systems. 65--68.
[12]
Eric PS Baumer. 2018. Socioeconomic Inequalities in the Non use of Facebook. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. 1--14.
[13]
Eric PS Baumer, Phil Adams, Vera D Khovanskaya, Tony C Liao, Madeline E Smith, Victoria Schwanda Sosik, and Kaiton Williams. 2013. Limiting, leaving, and (re) lapsing: an exploration of facebook non-use practices and experiences. In Proceedings of the SIGCHI conference on human factors in computing systems. 3257--3266.
[14]
Eric PS Baumer, Shion Guha, Emily Quan, David Mimno, and Geri K Gay. 2015. Missing photos, suffering withdrawal, or finding freedom? How experiences of social media non-use influence the likelihood of reversion. Social Media+ Society 1, 2 (2015), 2056305115614851.
[15]
Eric PS Baumer, Shion Guha, Patrick Skeba, and Geraldine Gay. 2019. All Users are (Not) Created Equal: Predictors Vary for Different Forms of Facebook Non/use. Proceedings of the ACM on Human-Computer Interaction 3, CSCW (2019), 1--28.
[16]
Jason Baumgartner, Savvas Zannettou, Brian Keegan, Megan Squire, and Jeremy Blackburn. 2020. The pushshift reddit dataset. In Proceedings of the International AAAI Conference on Web and Social Media, Vol. 14. 830--839.
[17]
Omri Ben-Shahar. 2019. Data Pollution. Journal of Legal Analysis 11 (2019), 104--159. Publisher: Narnia.
[18]
Ruha Benjamin. 2016. Informed refusal: Toward a justice-based bioethics. Science, Technology, & Human Values 41, 6 (2016), 967--990. Publisher: SAGE Publications Sage CA: Los Angeles, CA.
[19]
Battista Biggio, Blaine Nelson, and Pavel Laskov. 2012. Poisoning attacks against support vector machines. arXiv preprint arXiv:1206.6389 (2012).
[20]
Reuben Binns. 2018. Fairness in machine learning: Lessons from political philosophy. In Conference on Fairness, Accountability and Transparency. 149--159.
[21]
Michael Bloodgood and Chris Callison-Burch. 2010. Bucking the trend: large-scale cost-focused active learning for statistical machine translation. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 854--864.
[22]
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020. Language Models are Few-Shot Learners. arXiv:cs.CL/2005.14165
[23]
Jed R Brubaker, Mike Ananny, and Kate Crawford. 2016. Departing glances: A sociotechnical account of 'leaving' Grindr. New Media & Society 18, 3 (2016), 373--390.
[24]
Finn Brunton and Helen Fay Nissenbaum. 2015. Obfuscation: a user's guide for privacy and protest. MIT Press, Cambridge, Massachusetts.
[25]
Erik Brynjolfsson and Andrew McAfee. 2014. The second machine age: Work, progress, and prosperity in a time of brilliant technologies. WW Norton & Company.
[26]
Erik Brynjolfsson and Kristina McElheran. 2016. The rapid adoption of data-driven decision-making. American Economic Review 106, 5 (2016), 133--39.
[27]
Ceren Budak, Sharad Goel, Justin Rao, and Georgios Zervas. 2016. Understanding Emerging Threats to Online Advertising. In Proceedings of the 2016 ACM Conference on Economics and Computation (EC '16). ACM, New York, NY, USA, 561--578. https://doi.org/10.1145/2940716.2940787 event-place: Maastricht, The Netherlands.
[28]
Nathalie Casemajor, Stifmmode\acutee\elseé\fiphane Couture, Mauricio Delfin, Matthew Goerzen, and Alessandro Delfanti. 2015. Non-participation in digital media: toward a framework of mediated political action. Media, Culture & Society 37, 6 (May 2015), 850--866. https://doi.org/10.1177/0163443715584098 Publisher: SAGE Publications Ltd.
[29]
Stevie Chancellor, Eric PS Baumer, and Munmun De Choudhury. 2019. Who is the" Human" in Human-Centered Machine Learning: The Case of Predicting Mental Health from Social Media. Proceedings of the ACM on Human-Computer Interaction 3, CSCW (2019), 1--32.
[30]
Paul-Alexandru Chirita, Wolfgang Nejdl, and Cristian Zamfir. 2005. Preventing shilling attacks in online recommender systems. In Proceedings of the 7th annual ACM international workshop on Web information and data management. 67--74.
[31]
Junghwan Cho, Kyewook Lee, Ellie Shin, Garry Choy, and Synho Do. 2015. How much data is needed to train a medical image deep learning system to achieve necessary high accuracy? arXiv preprint arXiv:1511.06348 (2015).
[32]
Max Cho. 2011. Unsell Yourself---A Protest Model Against Facebook. Yale Law & Technology (2011).
[33]
Marika Cifor, Patricia Garcia, TL Cowan, Jasmine Rault, Tonia Sutherland, Anita Say Chan, Jennifer Rode, Anna Lauren Hoffmann, Niloufar Salehi, and Lisa Nakamura. 2019. Feminist data manifest-no.
[34]
Nick Couldry and Alison Powell. 2014. Big data from the bottom up. Big Data & Society 1, 2 (2014), 2053951714539277.
[35]
Kate Crawford and Vladan Joler. 2018. Anatomy of an AI System-The Amazon Echo as an anatomical map of human labor, data and planetary resources. AI Now Institute and Share Lab 7 (2018).
[36]
Thomas G Dietterich and Eun Bae Kong. 1995. Machine learning bias, statistical bias, and statistical variance of decision tree algorithms. Technical Report. Technical report, Department of Computer Science, Oregon State University.
[37]
Catherine D'Ignazio and Lauren F Klein. 2020. Data feminism. MIT Press.
[38]
Michaelanne Dye, David Nemer, Laura R Pina, Nithya Sambasivan, Amy S Bruckman, and Neha Kumar. 2017. Locating the Internet in the Parks of Havana. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. 3867--3878.
[39]
Christopher B Eiben, Justin B Siegel, Jacob B Bale, Seth Cooper, Firas Khatib, Betty W Shen, Barry L Stoddard, Zoran Popovic, and David Baker. 2012. Increased Diels-Alderase activity through backbone remodeling guided by Foldit players. Nature biotechnology 30, 2 (2012), 190--192.
[40]
Michael D. Ekstrand, Rezvan Joshaghani, and Hoda Mehrpouyan. 2018. Privacy for All: Ensuring Fair and Equitable Privacy Protections (Proceedings of Machine Learning Research), Sorelle A. Friedler and Christo Wilson (Eds.), Vol. 81. PMLR, New York, NY, USA, 35--47. http://proceedings.mlr.press/v81/ekstrand18a.html
[41]
Farzad Eskandanian, Nasim Sonboli, and Bamshad Mobasher. 2019. Power of the Few: Analyzing the Impact of Influential Users in Collaborative Recommender Systems. In Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization. 225--233.
[42]
Motahhare Eslami, Kristen Vaccaro, Karrie Karahalios, and Kevin Hamilton. 2017. " Be Careful; Things Can Be Worse than They Appear": Understanding Biased Algorithms and Users' Behavior Around Them in Rating Platforms. In ICWSM. 62--71.
[43]
Motahhare Eslami, Kristen Vaccaro, Min Kyung Lee, Amit Elazari Bar On, Eric Gilbert, and Karrie Karahalios. 2019. User attitudes towards algorithmic opacity and transparency in online reviewing platforms. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 1--14.
[44]
Virginia Eubanks. 2018. Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin's Press.
[45]
Minghong Fang, Neil Zhenqiang Gong, and Jia Liu. 2020. Influence Function based Data Poisoning Attacks to Top-N Recommender Systems. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3366423.3380072
[46]
Rosa L Figueroa, Qing Zeng-Treitler, Sasikiran Kandula, and Long H Ngo. 2012. Predicting sample size required for classification performance. BMC medical informatics and decision making 12, 1 (2012), 8. https://link.springer.com/article/10.1186/1472-6947-12-8 Publisher: Springer.
[47]
David Garcia, Pavlin Mavrodiev, and Frank Schweitzer. 2013. Social resilience in online communities: The autopsy of friendster. In Proceedings of the first ACM conference on Online social networks. 39--50.
[48]
Patricia Garcia, Tonia Sutherland, Marika Cifor, Anita Say Chan, Lauren Klein, Catherine D'Ignazio, and Niloufar Salehi. 2020. No: Critical Refusal as Feminist Data Practice. In Conference Companion Publication of the 2020 on Computer Supported Cooperative Work and Social Computing. 199--202.
[49]
Timnit Gebru. 2019. Oxford Handbook on AI Ethics Book Chapter on Race and Gender. arXiv preprint arXiv:1908.06165 (2019).
[50]
Jonas Geiping, Liam Fowl, W. Ronny Huang, Wojciech Czaja, Gavin Taylor, Michael Moeller, and Tom Goldstein. 2020. Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching. arXiv:cs.CV/2009.02276
[51]
Tarleton Gillespie. 2017. Algorithmically recognizable: Santorum's Google problem, and Google's Santorum problem. Information, communication & society 20, 1 (2017), 63--80.
[52]
Tarleton Gillespie. 2018. Custodians of the Internet: Platforms, content moderation, and the hidden decisions that shape social media. Yale University Press.
[53]
Michael Golebiewski and Danah Boyd. 2019. Data voids: Where missing data can easily be exploited. Data & Society (2019).
[54]
Ben Green. 2018. 'Fair' Risk Assessments: A Precarious Approach for Criminal Justice Reform. In 5th Workshop on fairness, accountability, and transparency in machine learning.
[55]
Rebecca Greenfield, Sarah Frier, and Ben Brody. 2018. NAACP Seeks Week-Long Facebook Boycott Over Racial Targeting. Bloomberg.com (Dec. 2018). https://www.bloomberg.com/news/articles/2018-12-17/naacp-calls-for-week-long-facebook-boycott-over-racial-targeting
[56]
Ihsan Gunes, Cihan Kaleli, Alper Bilge, and Huseyin Polat. 2014. Shilling attacks against recommender systems: a comprehensive survey. Artificial Intelligence Review 42, 4 (2014), 767--799. Publisher: Springer.
[57]
Michael Gurstein. 2011. Open data: Empowering the empowered or effective data use for everyone? First Monday 16 (02 2011). https://doi.org/10.5210/fm.v16i2.3316
[58]
Jeffrey T Hancock, Catalina Toma, and Nicole Ellison. 2007. The truth about lying in online dating profiles. In Proceedings of the SIGCHI conference on Human factors in computing systems. 449--452.
[59]
Kotaro Hara, Abigail Adams, Kristy Milland, Saiph Savage, Chris Callison-Burch, and Jeffrey P Bigham. 2018. A Data-Driven Analysis of Workers' Earnings on Amazon Mechanical Turk. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. ACM, 449.
[60]
B Hecht, L Wilcox, JP Bigham, J Schöning, E Hoque, J Ernst, Y Bisk, L De Russis, L Yarosh, B Anjum, and others. 2018. It's time to do something: Mitigating the negative impacts of computing through a change to the peer review process.
[61]
Joel Hestness, Sharan Narang, Newsha Ardalani, Gregory Diamos, Heewoo Jun, Hassan Kianinejad, Md Patwary, Mostofa Ali, Yang Yang, and Yanqi Zhou. 2017. Deep learning scaling is predictable, empirically. arXiv preprint arXiv:1712.00409 (2017).
[62]
Marit Hinnosaar, Toomas Hinnosaar, Michael E Kummer, and Olga Slivko. 2019. Wikipedia matters. Available at SSRN 3046400 (2019).
[63]
Daniel C Howe and Helen Nissenbaum. 2017. Engineering Privacy and Protest: A Case Study of AdNauseam. In IWPE@ SP. 57--64.
[64]
Kate Mathews Hunt. 2015. Gaming the system: Fake online reviews v. consumer law. Computer law & security review 31, 1 (2015), 3--25.
[65]
Sarah J Jackson, Moya Bailey, and Brooke Foucault Welles. 2020. # HashtagActivism: Networks of Race and Gender Justice. MIT Press.
[66]
Ross James. 2020. How to use Google Takeout to download your Google data-Business Insider. Business Insider (Jan 2020). https://www.businessinsider.com/what-is-google-takeout
[67]
Ruoxi Jia, David Dao, Boxin Wang, Frances Ann Hubis, Nick Hynes, Nezihe Merve Gürel, Bo Li, Ce Zhang, Dawn Song, and Costas J Spanos. 2019. Towards Efficient Data Valuation Based on the Shapley Value. In The 22nd International Conference on Artificial Intelligence and Statistics. 1167--1176.
[68]
Isaac L. Johnson, Yilun Lin, Toby Jia-Jun Li, Andrew Hall, Aaron Halfaker, Johannes Schöning, and Brent Hecht. 2016. Not at Home on the Range: Peer Production and the Urban/Rural Divide. Association for Computing Machinery, New York, NY, USA, 13--25. https://doi.org/10.1145/2858036.2858123
[69]
Charles I Jones and Christopher Tonetti. 2019. Nonrivalry and the Economics of Data. Technical Report. National Bureau of Economic Research.
[70]
Pang Wei W Koh, Kai-Siang Ang, Hubert Teo, and Percy S Liang. 2019. On the accuracy of influence functions for measuring group effects. In Advances in Neural Information Processing Systems. 5254--5264.
[71]
Sebastian Koos. 2012. What drives political consumption in Europe? A multilevel analysis on individual characteristics, opportunity structures and globalization. Acta Sociologica 55, 1 (March 2012), 37--57. https://doi.org/10.1177/0001699311431594
[72]
Robert E Kraut, Paul Resnick, Sara Kiesler, Moira Burke, Yan Chen, Niki Kittur, Joseph Konstan, Yuqing Ren, and John Riedl. 2012. Building successful online communities: Evidence-based social design. Mit Press.
[73]
Bogdan Kulynych, Rebekah Overdorf, Carmela Troncoso, and Seda Gürses. 2020. POTs:protective optimization technologies. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. 177--188.
[74]
Shamanth Kumar, Reza Zafarani, and Huan Liu. 2011. Understanding User Migration Patterns in Social Media. In AAAI, Vol. 11. 8--11.
[75]
Shyong K Lam and John Riedl. 2004. Shilling recommender systems for fun and profit. In Proceedings of the 13th international conference on World Wide Web. 393--402.
[76]
Cliff Lampe, Nicole B. Ellison, and Charles Steinfeld. 2008. Changes in Use and Perception of Facebook. In Proceedings of the 2008 conference on Computer supported cooperative work. 721--730.
[77]
Cliff Lampe, Jessica Vitak, and Nicole Ellison. 2013. Users and nonusers: Interactions between levels of adoption and social capital. In Proceedings of the 2013 conference on Computer supported cooperative work. 809--820.
[78]
Jong-Seok Lee and Dan Zhu. 2012. Shilling attack detection---a new approach for a trustworthy recommender system. INFORMS Journal on Computing 24, 1 (2012), 117--131. Publisher: INFORMS.
[79]
Min Kyung Lee, Daniel Kusbit, Evan Metsky, and Laura Dabbish. 2015. Working with machines: The impact of algorithmic and data-driven management on human workers. In Proceedings of the 33rd annual ACM conference on human factors in computing systems. 1603--1612.
[80]
Tuukka Lehtiniemi and Minna Ruckenstein. 2018. The social imaginaries of data activism. Big Data & Society 6, 1 (2018), 2053951718821146.
[81]
Bo Li, Yining Wang, Aarti Singh, and Yevgeniy Vorobeychik. 2016. Data poisoning attacks on factorization-based collaborative filtering. In Advances in neural information processing systems. 1885--1893.
[82]
Hanlin Li, Bodhi Alarcon, Sara M. Espinosa, and Brent Hecht. 2018. Out of Site: Empowering a New Approach to Online Boycotts. Proceedings of the 2018 Computer-Supported Cooperative Work and Social Computing (CSCW'2018/PACM) (2018).
[83]
Hanlin Li and Brent Hecht. 2020. 3 Stars on Yelp, 4 Stars on Google Maps: A Cross-Platform Examination of Restaurant Ratings. Proceedings of the ACM on Human-Computer Interaction 4, CSCW (2020).
[84]
Hanlin Li, Nicholas Vincent, Janice Tsai, Jofish Kaye, and Brent Hecht. 2019. How do people change their technology use in protest?: Understanding "protest users". Proceedings of the ACM on Human-Computer Interaction 3, CSCW (2019), 87.
[85]
Jiwei Li, Myle Ott, Claire Cardie, and Eduard Hovy. 2014. Towards a general rule for identifying deceptive opinion spam. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 1566--1576.
[86]
Kim Lyons. 2021. FTC settles with photo storage app that pivoted to facial recognition. The Verge (Jan. 2021). https://www.theverge.com/2021/1/11/22225171/ftc-facial-recognition-ever-settled-paravision-privacy-photos Publisher: The Verge.
[87]
Helen Margetts, Peter John, Scott Hale, and Taha Yasseri. 2015. Political turbulence: How social media shape collective action. Princeton University Press.
[88]
Arunesh Mathur, Jessica Vitak, Arvind Narayanan, and Marshini Chetty. 2018. Characterizing the use of browser-based blocking extensions to prevent online tracking. In Fourteenth Symposium on Usable Privacy and Security ({SOUPS} 2018). 103--116.
[89]
J Nathan Matias. 2016. Going dark: Social factors in collective action against platform operators in the Reddit blackout. In Proceedings of the 2016 CHI conference on human factors in computing systems. 1138--1151.
[90]
Connor McMahon, Isaac L Johnson, and Brent Hecht. 2017. The Substantial Interdependence of Wikipedia and Google: A Case Study on the Relationship Between Peer Production Communities and Information Technologies. In ICWSM. 142--151.
[91]
Stefania Milan and Lonneke Van der Velden. 2016. The alternative epistemologies of data activism. Digital Culture & Society 2, 2 (2016), 57--74.
[92]
Bamshad Mobasher, Robin Burke, Runa Bhaumik, and Chad Williams. 2005. Effective attack models for shilling item-based collaborative filtering systems. In Proceedings of the WebKDD Workshop. Citeseer, 13--23.
[93]
Yaroslav Nechaev, Francesco Corcoglioniti, and Claudio Giuliano. 2017. Concealing Interests of Passive Users in Social Media. In BlackMirror@ ISWC.
[94]
Edward Newell, David Jurgens, Haji Mohammad Saleem, Hardik Vala, Jad Sassine, Caitrin Armstrong, and Derek Ruths. 2016. User Migration in Online Social Networks: A Case Study on Reddit During a Period of Community Unrest. In ICWSM. 279--288.
[95]
Safiya Umoja Noble. 2018. Algorithms of oppression: How search engines reinforce racism. nyu Press.
[96]
Myle Ott, Claire Cardie, and Jeff Hancock. 2012. Estimating the prevalence of deception in online review communities. In Proceedings of the 21st international conference on World Wide Web. 201--210.
[97]
Kari Paul. 2020. Prime Day: activists protest against Amazon in cities across US. the Guardian (Apr 2020). https://www.theguardian.com/technology/2019/jul/15/prime-day-activists-plan-protests-in-us-cities-and-a-boycott-of-e-commerce-giant
[98]
Business Paul R. La Monica. 2020. Tech's magnificent seven are worth $7.7 trillion. https://www.cnn.com/2020/08/20/investing/faang-microsoft-tesla/index.html [Online; accessed 6. Oct. 2020].
[99]
Nikolaos Pitropakis, Emmanouil Panaousis, Thanassis Giannetsos, Eleftherios Anastasiadis, and George Loukas. 2019. A taxonomy and survey of attacks against machine learning. Computer Science Review 34 (Nov. 2019), 100199. https://doi.org/10.1016/j.cosrev.2019.100199 Publisher: Elsevier.
[100]
Laura Portwood-Stacer. 2013. Media refusal and conspicuous non-consumption: The performative and political dimensions of Facebook abstention. New Media & Society 15, 7 (2013), 1041--1057.
[101]
Eric A Posner and E Glen Weyl. 2018. Radical Markets: Uprooting Capitalism and Democracy for a Just Society. Princeton University Press.
[102]
Barbara Prainsack. 2019. Data donation: How to resist the iLeviathan. In The ethics of medical data donation. Springer, Cham, 9--22.
[103]
Alexander J Quinn and Benjamin B Bederson. 2011. Human computation: a survey and taxonomy of a growing field. In Proceedings of the SIGCHI conference on human factors in computing systems. ACM, 1403--1412.
[104]
Adam Satariano. 2020. What the G.D.P.R., Europe's Tough New Data Law, Means for You. N.Y. Times (May 2020). https://www.nytimes.com/2018/05/06/technology/gdpr-european-privacy-law.html Publisher: The New York Times Company.
[105]
Christine Satchell and Paul Dourish. 2009. Beyond the user: use and non-use in HCI. In Proceedings of the 21st Annual Conference of the Australian Computer-Human Interaction Special Interest Group: Design: Open 24/7. 9--16.
[106]
Devansh Saxena, Patrick Skeba, Shion Guha, and Eric PS Baumer. 2020. Methods for Generating Typologies of Non/use. Proceedings of the ACM on Human Computer Interaction 4, CSCW1 (2020), 1--26.
[107]
Sarita Yardi Schoenebeck. 2014. Giving up Twitter for Lent: how and why we take breaks from social media. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 773--782.
[108]
Roy Schwartz, Jesse Dodge, Noah A. Smith, and Oren Etzioni. 2020. Green AI. Commun. ACM 63, 12 (Nov. 2020), 54--63. https://doi.org/10.1145/3381831
[109]
Neil Selwyn. 2003. Apart from technology: understanding people's non-use of information and communication technologies in everyday life. Technology in society 25, 1 (2003), 99--116.
[110]
Alana Semuels. 2017. Why #DeleteUber and Other Boycotts Matter. Atlantic (Feb 2017). https://www.theatlantic.com/business/archive/2017/02/why-deleteuber-and-other-boycotts-matter/517416
[111]
Rijurekha Sen, Sohaib Ahmad, Amreesh Phokeer, Zaid Ahmed Farooq, Ihsan Ayyub Qazi, David Choffnes, and Krishna P Gummadi. 2017. Inside the walled garden: Deconstructing facebook's free basics program. ACM SIGCOMM Computer Communication Review 47, 5 (2017), 12--24.
[112]
Ali Shafahi, W Ronny Huang, Mahyar Najibi, Octavian Suciu, Christoph Studer, Tudor Dumitras, and Tom Goldstein. 2018. Poison frogs! targeted clean-label poisoning attacks on neural networks. In Advances in Neural Information Processing Systems. 6103--6113.
[113]
Shawn Shan, Emily Wenger, Jiayun Zhang, Huiying Li, Haitao Zheng, and Ben Y. Zhao. 2020. Fawkes: Protecting Privacy against Unauthorized Deep Learning Models. In 29th { USENIX} Security Symposium ({ USENIX} Security 20). 1589--1604.
[114]
Márcio Silva, Lucas Santos de Oliveira, Athanasios Andreou, Pedro Olmo Vaz de Melo, Oana Goga, and Fabrício Benevenuto. 2020. Facebook Ads Monitor: An Independent Auditing System for Political Ads on Facebook. In Proceedings of The Web Conference 2020. 224--234.
[115]
Jacob Steinhardt, Pang Wei W Koh, and Percy S Liang. 2017. Certified defenses for data poisoning attacks. In Advances in neural information processing systems. 3517--3529.
[116]
Stefan Stieger, Christoph Burger, Manuel Bohn, and Martin Voracek. 2013. Who commits virtual identity suicide? Differences in privacy concerns, internet addiction, and personality between Facebook users and quitters. Cyberpsychology, Behavior, and Social Networking 16, 9 (2013), 629--634. Publisher: Mary Ann Liebert, Inc. 140 Huguenot Street, 3rd Floor New Rochelle, NY 10801 USA.
[117]
Farnaz Tahmasebian, Li Xiong, Mani Sotoodeh, and Vaidy Sunderam. 2020. Crowdsourcing under data poisoning attacks: A comparative study. In IFIP Annual Conference on Data and Applications Security and Privacy. Springer, 310--332.
[118]
Catalina L Toma and Jeffrey T Hancock. 2010. Reading between the lines: linguistic cues to deception in online dating profiles. In Proceedings of the 2010 ACM conference on Computer supported cooperative work. 5--8.
[119]
Carmela Troncoso. 2019. Keynote Address: PETs, POTs, and Pitfalls: Rethinking the Protection of Users against Machine Learning. USENIX Association, Santa Clara, CA.
[120]
Max Van Kleek, Dave Murray-Rust, Amy Guy, Kieron O'Hara, and Nigel Shad-bolt. 2016. Computationally Mediated Pro-Social Deception. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (CHI '16). Association for Computing Machinery, New York, NY, USA, 552--563. https://doi.org/10.1145/2858036.2858060
[121]
Nicholas Vincent and Brent Hecht. 2021. Can "Conscious Data Contribution" Help Users to Exert "Data Leverage" Against Technology Companies? Proceedings of the ACM on Human-Computer Interaction CSCW.
[122]
Nicholas Vincent and Brent Hecht. 2021. A Deeper Investigation of the Importance of Wikipedia Links to the Success of Search Engines. Proceedings of the ACM on Human-Computer Interaction CSCW (2021).
[123]
Nicholas Vincent, Brent Hecht, and Shilad Sen. 2019. "Data Strikes": Evaluating the Effectiveness of New Forms of Collective Action Against Technology Platforms. In Proceedings of The Web Conference 2019.
[124]
Nicholas Vincent, Isaac Johnson, and Brent Hecht. 2018. Examining Wikipedia with a broader lens: Quantifying the value of Wikipedia's relationships with other large-scale online communities. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. 1--13.
[125]
Nicholas Vincent, Isaac Johnson, Patrick Sheehan, and Brent Hecht. 2019. Measuring the importance of user-generated content to search engines. In Proceedings of the International AAAI Conference on Web and Social Media, Vol. 13. 505--516.
[126]
Nicholas Vincent, Yichun Li, Renee Zha, and Brent Hecht. 2019. Mapping the Potential and Pitfalls of "Data Dividends" as a Means of Sharing the Profits of Artificial Intelligence. arXiv preprint arXiv:1912.00757 (2019).
[127]
Kaveh Waddell. 2020. California's New Privacy Rights Are Tough to Use, Consumer Reports Study Finds. Consum. Rep. (Oct 2020). https://www.consumerreports.org/privacy/californias-new-privacy-rights-are-tough-to-use
[128]
Daisuke Wakabayashi. 2018. California Passes Sweeping Law to Protect Online Privacy. N.Y. Times (Jun 2018). https://www.nytimes.com/2018/06/28/technology/california-online-privacy-law.html
[129]
Hongyi Wen, Longqi Yang, Michael Sobolev, and Deborah Estrin. 2018. Exploring recommendations under user-controlled data filtering. In Proceedings of the 12th ACM Conference on Recommender Systems. 72--76.
[130]
John Wilmhoff. 2017. Tom Brady literally owns the Jets, says Google search. https://www.espn.com/sportsnation/story/_/page/170727QTP_BradyOwnsJets/google-glitch-causes-tom-brady-appear-new-york-jets-owner%7D Publication Title: ESPN.
[131]
David C Wilson and Carlos E Seminario. 2013. When power users attack: assessing impacts in collaborative recommender systems. In Proceedings of the 7th ACM conference on Recommender systems. 427--430.
[132]
David C Wilson and Carlos E Seminario. 2014. Evil twins: Modeling power users in attacks on recommender systems. In International Conference on User Modeling, Adaptation, and Personalization. Springer, 231--242.
[133]
Sally ME Wyatt. 2003. Non-users also matter: The construction of users and non-users of the Internet. Now users matter:The co-construction of users and technology (2003), 67--79.
[134]
Sean Xin Xu and Xiaoquan (Michael) Zhang. 2013. Impact of Wikipedia on Market Information Environment: Evidence on Management Disclosure and Investor Reaction. MIS Quarterly 37, 4 (Dec 2013), 1043--1068. http://www.jstor.org/stable/43825781
[135]
Haoqi Zhang, Andrés Monroy-Hernández, Aaron Shaw, Sean A Munson, Elizabeth Gerber, Benjamin Mako Hill, Peter Kinnaird, Shelly D Farnham, and Patrick Minder. 2014. WeDo: end-to-end computer supported collective action. In Eighth International AAAI Conference on Weblogs and Social Media.
[136]
Renjie Zhou, Samamon Khemmarat, and Lixin Gao. 2010. The impact of YouTube recommendation system on video views. In Proceedings of the 10th ACM SIGCOMM conference on Internet measurement. ACM, 404--410.

Cited By

View all
  • (2024)Unveiling Two-Fold Gamification: Exploring the Agency Of DeliveryWorkers in Urban IndiaProceedings of the 7th ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies10.1145/3674829.3675066(117-126)Online publication date: 8-Jul-2024
  • (2024)What Can AI Ethics Learn from Anarchism?XRDS: Crossroads, The ACM Magazine for Students10.1145/366559430:4(22-25)Online publication date: 28-Jun-2024
  • (2024)Building, Shifting, & Employing Power: A Taxonomy of Responses From Below to Algorithmic HarmProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658958(1093-1106)Online publication date: 3-Jun-2024
  • Show More Cited By

Index Terms

  1. Data Leverage: A Framework for Empowering the Public in its Relationship with Technology Companies

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    FAccT '21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency
    March 2021
    899 pages
    ISBN:9781450383097
    DOI:10.1145/3442188
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 01 March 2021

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. conscious data contribution
    2. data leverage
    3. data poisoning
    4. data strikes

    Qualifiers

    • Research-article
    • Research
    • Refereed limited

    Funding Sources

    Conference

    FAccT '21
    Sponsor:

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)679
    • Downloads (Last 6 weeks)88
    Reflects downloads up to 17 Oct 2024

    Other Metrics

    Citations

    Cited By

    View all
    • (2024)Unveiling Two-Fold Gamification: Exploring the Agency Of DeliveryWorkers in Urban IndiaProceedings of the 7th ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies10.1145/3674829.3675066(117-126)Online publication date: 8-Jul-2024
    • (2024)What Can AI Ethics Learn from Anarchism?XRDS: Crossroads, The ACM Magazine for Students10.1145/366559430:4(22-25)Online publication date: 28-Jun-2024
    • (2024)Building, Shifting, & Employing Power: A Taxonomy of Responses From Below to Algorithmic HarmProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658958(1093-1106)Online publication date: 3-Jun-2024
    • (2024)AI Failure Cards: Understanding and Supporting Grassroots Efforts to Mitigate AI Failures in Homeless ServicesProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658935(713-732)Online publication date: 3-Jun-2024
    • (2024)Data Agency Theory: A Precise Theory of Justice for AI ApplicationsProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658930(631-641)Online publication date: 3-Jun-2024
    • (2024)To See or Not to See: Understanding the Tensions of Algorithmic Curation for Visual ArtsProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658917(444-455)Online publication date: 3-Jun-2024
    • (2024)A Framework for Exploring the Consequences of AI-Mediated Enterprise Knowledge Access and Identifying Risks to WorkersProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658900(207-220)Online publication date: 3-Jun-2024
    • (2024)Data Probes as Boundary Objects for Technology Policy Design: Demystifying Technology for Policymakers and Aligning Stakeholder Objectives in Rideshare Gig WorkProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642000(1-21)Online publication date: 11-May-2024
    • (2023)Algorithmic collective action in machine learningProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3618918(12570-12586)Online publication date: 23-Jul-2023
    • (2023)Data Refusal from Below: A Framework for Understanding, Evaluating, and Envisioning Refusal as DesignACM Journal on Responsible Computing10.1145/36301071:1(1-23)Online publication date: 25-Oct-2023
    • Show More Cited By

    View Options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Get Access

    Login options

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media