R.X. Schwartz
Panagiotis Apostolellis
ABSTRACT
Many users take advantage of digital self-control tools to self-regulate their device usage through interventions such as timers and lockout mechanisms. One of the major challenges faced by these tools is the user reacting against their self-imposed constraints and abandoning the tool. Although lower-risk interventions would reduce the likelihood of abandonment, previous research on digital self-control tools has left this area of study relatively unexplored. In response, this paper contributes two foundational principles relating risk and effectiveness; four widely applicable novel design patterns for reducing risk of abandonment of digital self-control tools (continuously variable interventions, anti-aging design, obligatory bundling of interventions, and intermediary control systems); and a prototype digital self-control tool that implements these four low-risk design patterns.
- Alex Hern. 2014. Why Google has 200m reasons to put engineers over designers. The Guardian. Retrieved February 25, 2021 from https://www.theguardian.com/technology/2014/feb/05/why-google-engineers-designersGoogle Scholar
- Laura M. Holson,. 2009. Putting a Bolder Face on Google. The New York Times. Retrieved February 25, 2021 from https://www.nytimes.com/2009/03/01/business/01marissa.html?pagewanted=printGoogle Scholar
- Alex Deng and Xiaolin Shi. 2016. Data-Driven Metric Development for Online Controlled Experiments: Seven Lessons Learned. In Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’16), 77–86. https://doi.org/10.1145/2939672.2939700Google ScholarDigital Library
- Ron Kohavi. 2015. Online Controlled Experiments: Lessons from Running A/B/n Tests for 12 Years. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD ’15, 1–1. https://doi.org/10.1145/2783258.2785464Google ScholarDigital Library
- Ron Kohavi, Roger Longbotham, Dan Sommerfield, and Randal M. Henne. 2009. Controlled experiments on the web: survey and practical guide. Data Mining and Knowledge Discovery 18, 1: 140–181. https://doi.org/10.1007/s10618-008-0114-1Google ScholarDigital Library
- Kerry Rodden, Hilary Hutchinson, and Xin Fu. 2010. Measuring the User Experience on a Large Scale: User-centered Metrics for Web Applications. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’10), 2395–2398. https://doi.org/10.1145/1753326.1753687Google ScholarDigital Library
- Engin Bozdag. 2013. Bias in algorithmic filtering and personalization. Ethics and Information Technology 15, 3: 209–227. https://doi.org/10.1007/s10676-013-9321-6Google ScholarDigital Library
- Douglas McIlwraith, Haralambos Marmanis, and Dmitry Babenko. 2016. Algorithms of the Intelligent Web. Manning Publications Co., Greenwich, CT, USA.Google Scholar
- Ansgar Koene, Elvira Perez, Christopher James Carter, Ramona Statache, Svenja Adolphs, Claire O'Malley, Tom Rodden, and Derek McAuley. 2015. Ethics of Personalized Information Filtering. In Internet Science, Thanassis Tiropanis, Athena Vakali, Laura Sartori and Pete Burnap (eds.). Springer International Publishing, Cham, 123–132. https://doi.org/10.1007/978-3-319-18609-2_10Google ScholarCross Ref
- Dimitris Paraschakis. 2017. Towards an ethical recommendation framework. In 2017 11th International Conference on Research Challenges in Information Science (RCIS), 211–220. https://doi.org/10.1109/RCIS.2017.7956539Google ScholarCross Ref
- Royal Society for Public Health. 2017. #StatusOfMind: Social media and young people's mental health and wellbeing. Retrieved February 25, 2021 from https://www.rsph.org.uk/static/uploaded/d125b27c-0b62-41c5-a2c0155a8887cd01.pdfGoogle Scholar
- Elroy Boers, Mohammad H. Afzali, Nicola Newton, and Patricia Conrod. 2019. Association of Screen Time and Depression in Adolescence. JAMA Pediatrics. https://doi.org/10.1001/jamapediatrics.2019.1759Google ScholarCross Ref
- Kevin Roose. 2018. Is Tech Too Easy to Use? The New York Times. Retrieved February 25, 2021 from https://www.nytimes.com/2018/12/12/technology/tech-friction-frictionless.htmlGoogle Scholar
- Alexis C. Madrigal. 2018. How YouTube's Algorithm Really Works. The Atlantic. Retrieved February 25, 2021 from https://www.theatlantic.com/technology/archive/2018/11/how-youtubes-algorithm-really-works/575212/Google Scholar
- Miguel Helft. 2009. Should Design Be Held Back by a Tyranny of Data? The New York Times. Retrieved February 25, 2021 from https://www.nytimes.com/2009/05/10/business/10ping.htmlGoogle Scholar
- Rachel Lerman. 2019. Q&A: Ex-Googler Harris on how tech “downgrades” humans. AP NEWS. Retrieved February 25, 2021 from https://apnews.com/dea7f32d16364c6093f19b938370d600Google Scholar
- Robert Gorwa. 2019. What is platform governance? Information, Communication & Society 22, 6: 854–871. https://doi.org/10.1080/1369118X.2019.1573914Google ScholarCross Ref
- Anna L. Cox, Sandy J.J. Gould, Marta E. Cecchinato, Ioanna Iacovides, and Ian Renfree. 2016. Design Frictions for Mindful Interactions: The Case for Microboundaries. In Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems - CHI EA ’16, 1389–1397. https://doi.org/10.1145/2851581.2892410Google ScholarDigital Library
- Eric P.S. 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 (CHI ’13), 3257–3266. https://doi.org/10.1145/2470654.2466446Google ScholarDigital Library
- Kevin Witzenberger. 2018. The Hyperdodge: How Users Resist Algorithmic Objects in Everyday Life. Media Theory 2, 2: 29–51. Retrieved February 25, 2021 from https://hal.archives-ouvertes.fr/hal-02047585Google Scholar
- Aaron Smith. 2018. Public Attitudes Toward Computer Algorithms. Pew Research Center: Internet, Science & Tech. Retrieved February 25, 2021 from https://www.pewinternet.org/2018/11/16/public-attitudes-toward-computer-algorithms/Google Scholar
- Jesse Fox and Jennifer J. Moreland. 2015. The dark side of social networking sites: An exploration of the relational and psychological stressors associated with Facebook use and affordances. Computers in Human Behavior 45: 168–176. https://doi.org/10.1016/j.chb.2014.11.083Google ScholarDigital Library
- R.X. Schwartz. 2019. Ulysses’ ropes and the inherent limits of digital self-control tools. In 5th International Conference on the History and Philosophy of Computing. https://doi.org/10.18130/v3-dfzq-ny16Google ScholarCross Ref
- Ulrik Lyngs, Kai Lukoff, Petr Slovak, Reuben Binns, Adam Slack, Michael Inzlicht, Max Van Kleek, and Nigel Shadbolt. 2019. Self-Control in Cyberspace: Applying Dual Systems Theory to a Review of Digital Self-Control Tools. arXiv:1902.00157 [cs]. https://doi.org/10.1145/3290605.3300361Google ScholarDigital Library
- Alberto Monge Roffarello and Luigi De Russis. 2019. The Race Towards Digital Wellbeing: Issues and Opportunities. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems - CHI ’19, 1–14. https://doi.org/10.1145/3290605.3300616Google ScholarDigital Library
- Charlie Pinder, Jo Vermeulen, Benjamin R. Cowan, and Russell Beale. 2018. Digital Behaviour Change Interventions to Break and Form Habits. ACM Trans. Comput.-Hum. Interact. 25, 3: 15:1-15:66. https://doi.org/10.1145/3196830Google ScholarDigital Library
- Jack Nicas. 2019. Apple Cracks Down on Apps That Fight iPhone Addiction. The New York Times. Retrieved February 25, 2021 from https://www.nytimes.com/2019/04/27/technology/apple-screen-time-trackers.htmlGoogle Scholar
- Geza Kovacs, Zhengxuan Wu, and Michael S. Bernstein. 2018. Rotating Online Behavior Change Interventions Increases Effectiveness But Also Increases Attrition. Proc. ACM Hum.-Comput. Interact. 2, CSCW: 95:1-95:25. https://doi.org/10.1145/3274364Google ScholarDigital Library
- Ana Caraban, Evangelos Karapanos, Daniel Gonçalves, and Pedro Campos. 2019. 23 Ways to Nudge: A Review of Technology-Mediated Nudging in Human-Computer Interaction. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems - CHI ’19, 1–15. https://doi.org/10.1145/3290605.3300733Google ScholarDigital Library
- Katarzyna Stawarz, Anna L. Cox, and Ann Blandford. 2015. Beyond Self-Tracking and Reminders: Designing Smartphone Apps That Support Habit Formation. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems - CHI ’15, 2653–2662. https://doi.org/10.1145/2702123.2702230Google ScholarDigital Library
- Charlie Pinder. 2018. Targeting the automatic: Nonconscious behaviour change using technology. University of Birmingham. Retrieved from https://etheses.bham.ac.uk/id/eprint/8539/1/Pinder18PhD.pdfGoogle Scholar
- Alexander T. Adams, Jean Costa, Malte F. Jung, and Tanzeem Choudhury. 2015. Mindless computing: designing technologies to subtly influence behavior. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing - UbiComp ’15, 719–730. https://doi.org/10.1145/2750858.2805843Google ScholarDigital Library
- Jonathan St. B. T. Evans. 2007. Dual-Processing Accounts of Reasoning, Judgment, and Social Cognition. Annual Review of Psychology 59, 1: 255–278. https://doi.org/10.1146/annurev.psych.59.103006.093629Google ScholarCross Ref
- Shea Houlihan. 2018. Dual-process models of health-related behaviour and cognition: a review of theory. Public Health 156: 52–59. https://doi.org/10.1016/j.puhe.2017.11.002Google ScholarCross Ref
- Jonathan St B. T. Evans. 2009. How many dual-process theories do we need? One, two, or many? Oxford University Press.Google Scholar
- Daniel Kahneman. 2011. Thinking, Fast and Slow. Farrar, Straus and Giroux.Google Scholar
- Valerie A. Thompson. 2014. Chapter Two - What Intuitions Are… and Are Not. In Psychology of Learning and Motivation, Brian H. Ross (ed.). Academic Press, 35–75. https://doi.org/10.1016/B978-0-12-800090-8.00002-0Google ScholarCross Ref
- Daniel Kahneman and Shane Frederick. 2002. Representativeness revisited: Attribute substitution in intuitive judgment. https://doi.org/10.1017/cbo9780511808098.004Google ScholarCross Ref
- Jaap Ham and Cees Midden. 2010. Ambient Persuasive Technology Needs Little Cognitive Effort: The Differential Effects of Cognitive Load on Lighting Feedback versus Factual Feedback. In Persuasive Technology (Lecture Notes in Computer Science), 132–142.Google Scholar
- Jonathan A. Tran, Katie S. Yang, Katie Davis, and Alexis Hiniker. 2019. Modeling the Engagement-Disengagement Cycle of Compulsive Phone Use. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems - CHI ’19, 1–14. https://doi.org/10.1145/3290605.3300542Google ScholarDigital Library
- Marjolein Lanzing. 2019. “Strongly Recommended” Revisiting Decisional Privacy to Judge Hypernudging in Self-Tracking Technologies. Philosophy & Technology 32, 3: 549–568. https://doi.org/10.1007/s13347-018-0316-4Google ScholarCross Ref
- Rebecca Gulotta, Jodi Forlizzi, Rayoung Yang, and Mark Wah Newman. 2016. Fostering Engagement with Personal Informatics Systems. In Proceedings of the 2016 ACM Conference on Designing Interactive Systems (DIS ’16), 286–300. https://doi.org/10.1145/2901790.2901803Google ScholarDigital Library
- Ian Renfree, Daniel Harrison, Paul Marshall, Katarzyna Stawarz, and Anna Cox. 2016. Don't Kick the Habit: The Role of Dependency in Habit Formation Apps. In Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems - CHI EA ’16, 2932–2939. https://doi.org/10.1145/2851581.2892495Google ScholarDigital Library
- Philip Brey. 2005. Freedom and Privacy in Ambient Intelligence. Ethics and Information Technology 7, 3: 157–166. https://doi.org/10.1007/s10676-006-0005-3Google ScholarDigital Library
- Valeria Orso, Renato Mazza, Luciano Gamberini, Ann Morrison, and Walther Jensen. 2017. Investigating Tactile Stimulation in Symbiotic Systems. In Symbiotic Interaction (Lecture Notes in Computer Science), 137–142.Google Scholar
- Anne-Marie Brouwer, Loïs van de Water, Maarten Hogervorst, Wessel Kraaij, Jan Maarten Schraagen, and Koen Hogenelst. 2018. Monitoring mental state during real life office work. In Symbiotic Interaction: 6th International Workshop, Symbiotic 2017 Eindhoven, The Netherlands, December 18-19, 2017. Revised Selected Papers, 18–29. https://doi.org/10.1007/978-3-319-91593-7_3Google ScholarCross Ref
- Evangelos Karapanos. 2015. Sustaining User Engagement with Behavior-change Tools. interactions 22, 4: 48–52. https://doi.org/10.1145/2775388Google ScholarDigital Library
- Charlie Pinder, Jo Vermeulen, Russell Beale, and Robert Hendley. 2015. Exploring Nonconscious Behaviour Change Interventions on Mobile Devices. In Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct - MobileHCI ’15, 1010–1017. https://doi.org/10.1145/2786567.2794319Google ScholarDigital Library
- Charlie Pinder, Jo Vermeulen, Benjamin R. Cowan, Russell Beale, and Robert J. Hendley. 2017. Exploring the feasibility of subliminal priming on smartphones. In Proceedings of the 19th International Conference on Human-Computer Interaction with Mobile Devices and Services - MobileHCI ’17, 1–15. https://doi.org/10.1145/3098279.3098531Google ScholarDigital Library
- Oswald Barral, Gabor Aranyi, Sid Kouider, Alan Lindsay, Hielke Prins, Imtiaj Ahmed, Giulio Jacucci, Paolo Negri, Luciano Gamberini, David Pizzi, and Marc Cavazza. 2014. Covert Persuasive Technologies: Bringing Subliminal Cues to Human-Computer Interaction. In Persuasive Technology (Lecture Notes in Computer Science), 1–12.Google Scholar
- Miguel Barreda-Ángeles, Ioannis Arapakis, Xiao Bai, B. Barla Cambazoglu, and Alexandre Pereda-Baños. 2015. Unconscious Physiological Effects of Search Latency on Users and Their Click Behaviour. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR ’15, 203–212. https://doi.org/10.1145/2766462.2767719Google ScholarDigital Library
- Eric Schurman and Jake Brutlag. 2009. The user and business impact of server delays, additional bytes, and http chunking in web search. In Velocity Web Performance and Operations Conference.Google Scholar
- Yang Song, Xiaolin Shi, and Xin Fu. 2013. Evaluating and Predicting User Engagement Change with Degraded Search Relevance. In Proceedings of the 22Nd International Conference on World Wide Web (WWW ’13), 1213–1224. https://doi.org/10.1145/2488388.2488494Google ScholarDigital Library
- Ron Kohavi, Alex Deng, Brian Frasca, Roger Longbotham, Toby Walker, and Ya Xu. 2012. Trustworthy Online Controlled Experiments: Five Puzzling Outcomes Explained. In KDD ’12 Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, 9.Google ScholarDigital Library
- Timothy J. Pleskac and Ralph Hertwig. 2014. Ecologically rational choice and the structure of the environment. Journal of Experimental Psychology: General 143, 5: 2000–2019. https://doi.org/10.1037/xge0000013Google ScholarCross Ref
- Devansh Saxena, Patrick Skeba, Shion Guha, and Eric P. S. Baumer. 2020. Methods for Generating Typologies of Non/use. Proceedings of the ACM on Human-Computer Interaction 4, CSCW1: 1–26. https://doi.org/10.1145/3392832Google ScholarDigital Library
- David Lorge Parnas. 1994. Software aging. In Proceedings of the 16th international conference on Software engineering (ICSE ’94), 279–287.Google ScholarDigital Library
- 2020. HabitLab. Retrieved from https://chrome.google.com/webstore/detail/habitlab/obghclocpdgcekcognpkblghkedcpdgd?hl=enGoogle Scholar
- Casey Fiesler. 2020. Lawful Users: Copyright Circumvention and Legal Constraints on Technology Use. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–11. https://doi.org/10.1145/3313831.3376745Google ScholarDigital Library
- 2016. Blocked Facebook ads unblocked, for now. BBC News. Retrieved February 25, 2021 from https://www.bbc.com/news/technology-37056013Google Scholar
- J. von Neumann. 1956. Probabilistic Logics and the Synthesis of Reliable Organisms From Unreliable Components. In Automata Studies. (AM-34), C. E. Shannon and J. McCarthy (eds.). Princeton University Press, Princeton, 43–98. https://doi.org/10.1515/9781400882618-003Google ScholarCross Ref
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- Reducing Risk in Digital Self-Control Tools: Design Patterns and Prototype
Index terms have been assigned to the content through auto-classification.
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