research-article

Communicating Algorithmic Process in Online Behavioral Advertising

Authors:

Motahhare Eslami,

Sneha R. Krishna Kumaran,

Christian Sandvig,

Karrie KarahaliosAuthors Info & Claims

CHI '18: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

Paper No.: 432, Pages 1 - 13

https://doi.org/10.1145/3173574.3174006

Published: 21 April 2018 Publication History

Abstract

Advertisers develop algorithms to select the most relevant advertisements for users. However, the opacity of these algorithms, along with their potential for violating user privacy, has decreased user trust and preference in behavioral advertising. To mitigate this, advertisers have started to communicate algorithmic processes in behavioral advertising. However, how revealing parts of the algorithmic process affects users' perceptions towards ads and platforms is still an open question. To investigate this, we exposed 32 users to why an ad is shown to them, what advertising algorithms infer about them, and how advertisers use this information. Users preferred interpretable, non-creepy explanations about why an ad is presented, along with a recognizable link to their identity. We further found that exposing users to their algorithmically-derived attributes led to algorithm disillusionment---users found that advertising algorithms they thought were perfect were far from it. We propose design implications to effectively communicate information about advertising algorithms.

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References

[1]

Jagdish Prasad Achara, Javier Parra-Arnau, and Claude Castelluccia. 2016. Mytrackingchoices: Pacifying the ad-block war by enforcing user privacy preferences. arXiv preprint arXiv:1604.04495 (2016).

[2]

Athanasios Andreou, Giridhari Venkatadri, Oana Goga, Krishna P Gummadi, Patrick Loiseau, and Alan Mislove. 2018. Investigating Ad Transparency Mechanisms in Social Media: A Case Study of Facebook's Explanations. In The Network and Distributed System Security Symposium (NDSS).

[3]

Julio Angulo, Simone Fischer-H¨ ubner, Tobias Pulls, and Erik W¨ astlund. 2015. Usable transparency with the data track: a tool for visualizing data disclosures. In Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems. ACM, 1803--1808.

Digital Library

[4]

Howard Beales. 2010. The value of behavioral targeting. Network Advertising Initiative 1 (2010).

[5]

Matthew Chalmers and Ian MacColl. 2003. Seamful and seamless design in ubiquitous computing. In Workshop at the crossroads: The interaction of HCI and systems issues in UbiComp, Vol. 8.

[6]

John Cheney-Lippold. 2011. A new algorithmic identity: Soft biopolitics and the modulation of control. Theory, Culture & Society 28, 6 (2011), 164--181.

[7]

Federal Trade Commision. 2011. Facebook Settles FTC Charges That It Deceived Consumers By Failing To Keep Privacy Promises. (2011). http://bit.ly/1U44BfY.

[8]

U.S. Equal Employment Opportunity Commission. retrieved on Dec 2017. U.S. Equal Employment Opportunity Commission: Discrimination by Type. (retrieved on Dec 2017). https://www.eeoc.gov/laws/types/.

[9]

Connectio. 2015. 25 Weirdly Specific Facebook Ads Targeting Ideas You Didn't Know Existed. (2015). http://connectio.io/25-facebook-ads-targeting-ideas/.

[10]

Matthew Crain. 2016. The limits of transparency: Data brokers and commodification. new media & society (2016).

[11]

Nicholas Diakopoulos. 2014. Algorithmic-Accountability: the investigation of Black Boxes. Tow Center for Digital Journalism (2014).

[12]

Nicholas Diakopoulos and Michael Koliska. 2017. Algorithmic transparency in the news media. Digital Journalism 5, 7 (2017), 809--828.

[13]

Motahhare Eslami, Karrie Karahalios, Christian Sandvig, Kristen Vaccaro, Aimee Rickman, Kevin Hamilton, and Alex Kirlik. 2016. First I "like" it, then I hide it: Folk Theories of Social Feeds. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM, 2371--2382.

Digital Library

[14]

Motahhare Eslami, Aimee Rickman, Kristen Vaccaro, Amirhossein Aleyasen, Andy Vuong, Karrie Karahalios, Kevin Hamilton, and Christian Sandvig. 2015. I always assumed that I wasn't really that close to {her}: Reasoning about Invisible Algorithms in News Feeds. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. ACM, 153--162.

Digital Library

[15]

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.

[16]

The Office for Creative Research. 2013. Behind the Banner: A visualization of the adtech ecosystem. (2013). http://o-c-r.org/behindthebanner/.

[17]

Lawrence M. Friedman. 1999. The Horizontal Society. New Haven, CT: Yale University Press.

[18]

Gerhard Friedrich and Markus Zanker. 2011. A taxonomy for generating explanations in recommender systems. AI Magazine 32, 3 (2011), 90--98.

Digital Library

[19]

Tarleton Gillespie. 2014. The relevance of algorithms. Media technologies: Essays on communication, materiality, and society 167 (2014).

[20]

Bryce Goodman and Seth Flaxman. 2016. EU regulations on algorithmic decision-making and a right to explanation. In ICML Workshop on Human Interpretability in Machine Learning.

[21]

Jonathan L Herlocker, Joseph A Konstan, and John Riedl. 2000. Explaining collaborative filtering recommendations. In Proceedings of the 2000 ACM conference on Computer supported cooperative work. ACM, 241--250.

Digital Library

[22]

Kashmir Hill. 2017. Facebook Figured Out My Family Secrets, And It Won't Tell Me How. (2017). http://bit.ly/2xjBorJ.

[23]

Facebook Inc. 2016. How Facebook decide for a person is Technology Early Adopter or not? (2016). https://www.facebook.com/business/help/community/ question/?id=10153713741703882.

[24]

Facebook Inc. Retrieved on Dec 2017a. What are my ad preferences and how can I adjust them? (Retrieved on Dec 2017). https://www.facebook.com/help/247395082112892.

[25]

Google Inc. Retrieved on Dec 2017b. About Ads Settings. (Retrieved on Dec 2017). https://support.google.com/ ads/answer/2662856?hl=en&ref_topic=7048998.

[26]

Farnaz Jahanbakhsh, Wai-Tat Fu, Karrie Karahalios, Darko Marinov, and Brian Bailey. 2017. You Want Me to Work with Who?: Stakeholder Perceptions of Automated Team Formation in Project-based Courses. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. ACM, 3201--3212.

Digital Library

[27]

Terry Parris Jr. Julia Angwin and Surya Mattu. 2016. BREAKING THE BLACK BOX: What Facebook Knows About You. (2016). https://www.propublica.org/article/ breaking-the-black-box-what-facebook-knows-about-you.

[28]

Vera Khovanskaya, Maria Bezaitis, and Phobe Sengers. 2016. The case of the strangerationist: Re-interpreting critical technical practice. In Proceedings of the 2016 ACM Conference on Designing Interactive Systems. ACM, 134--145.

Digital Library

[29]

Ren´ e F Kizilcec. 2016. How much information?: Effects of transparency on trust in an algorithmic interface. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM, 2390--2395.

Digital Library

[30]

Todd Kulesza, Simone Stumpf, Margaret Burnett, Sherry Yang, Irwin Kwan, and Weng-Keen Wong. 2013. Too much, too little, or just right? Ways explanations impact end users' mental models. In Visual Languages and Human-Centric Computing (VL/HCC), 2013 IEEE Symposium on. IEEE, 3--10.

[31]

Pedro Giovanni Leon, Justin Cranshaw, Lorrie Faith Cranor, Jim Graves, Manoj Hastak, Blase Ur, and Guzi Xu. 2012. What do online behavioral advertising privacy disclosures communicate to users?. In Proceedings of the 2012 ACM workshop on Privacy in the electronic society. ACM, 19--30.

Digital Library

[32]

Bin Liu, Anmol Sheth, Udi Weinsberg, Jaideep Chandrashekar, and Ramesh Govindan. 2013. AdReveal: improving transparency into online targeted advertising. In Proceedings of the 12th ACM Workshop on Hot Topics in Networks. ACM, 12.

Digital Library

[33]

Christopher T Marsden. 2011. Internet co-regulation: European law, regulatory governance and legitimacy in cyberspace. Cambridge University Press.

[34]

Helen Nissenbaum. 2009. Privacy in context: Technology, policy, and the integrity of social life. Stanford University Press.

Digital Library

[35]

Nvivo 2017. Nvivo 11. (2017). http://www.qsrinternational.com/nvivo-product.

[36]

Changhoon Oh, Taeyoung Lee, Yoojung Kim, SoHyun Park, Bongwon Suh, and others. 2017. Us vs. Them: Understanding Artificial Intelligence Technophobia over the Google DeepMind Challenge Match. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. ACM, 2523--2534.

Digital Library

[37]

Lara O'Reilly. 2014. Here's One Way To Find Out Which Advertisers Are Tracking You Across The Internet. (2014). http://www.businessinsider.com/ floodwatch-ad-tracking-chrome-extension-2014--10.

[38]

Javier Parra-Arnau, Jagdish Prasad Achara, and Claude Castelluccia. 2017. MyAdChoices: Bringing transparency and control to online advertising. ACM Transactions on the Web (TWEB) 11, 1 (2017), 7.

Digital Library

[39]

Frank Pasquale. 2015. The black box society: The secret algorithms that control money and information. Harvard University Press.

[40]

Pearl Pu and Li Chen. 2007. Trust-inspiring explanation interfaces for recommender systems. Knowledge-Based Systems 20, 6 (2007), 542--556.

Digital Library

[41]

Emilee J Rader. 2014. Awareness of Behavioral Tracking and Information Privacy Concern in Facebook and Google. In SOUPS. 51--67.

[42]

Ashwini Rao, Florian Schaub, and Norman Sadeh. 2015. What do they know about me? Contents and concerns of online behavioral profiles. arXiv preprint arXiv:1506.01675 (2015).

[43]

Christian Sandvig, Kevin Hamilton, Karrie Karahalios, and Cedric Langbort. 2014. Auditing algorithms: Research methods for detecting discrimination on internet platforms. Data and discrimination: converting critical concerns into productive inquiry (2014).

[44]

Florian Schaub, Aditya Marella, Pranshu Kalvani, Blase Ur, Chao Pan, Emily Forney, and L Cranor. 2016. Watching them watching me: Browser extensions impact on user privacy awareness and concern. In NDSS Workshop on Usable Security.

[45]

Doc Searls. 2012. The intention economy: when customers take charge. Harvard Business Press.

[46]

Nick Seaver. 2013. Knowing algorithms. Media in Transition 8 (2013), 1--12.

[47]

Rashmi Sinha and Kirsten Swearingen. 2002. The role of transparency in recommender systems. In CHI'02 extended abstracts on Human factors in computing systems. ACM, 830--831.

Digital Library

[48]

Aaron Springer, Victoria Hollis, and Whittaker Steve. 2017. Dice in the Black Box: User Experiences with an Inscrutable Algorithm. In The AAAI 2017 Spring Symposium on Designing the User Experience of Machine Learning Systems. AAAI.

[49]

Darren M Stevenson. 2016. Data, Trust, and Transparency in Personalized Advertising. (2016).

[50]

Andrew Tate. 2017. The 15 Strangest Targeting Categories on Facebook. (2017). https://adespresso.com/blog/ the-15-strangest-targeting-categories-on-facebook/.

[51]

Omer Tene and Jules Polenetsky. 2012. To track or do not track: advancing transparency and individual control in online behavioral advertising. Minn. JL Sci. & Tech. 13 (2012), 281.

[52]

Joseph Turow. 2017. The Aisles Have Eyes: How Retailers Track Your Shopping, Strip Your Privacy, and Define Your Power. Yale University Press.

[53]

Blase Ur, Pedro Giovanni Leon, Lorrie Faith Cranor, Richard Shay, and Yang Wang. 2012. Smart, useful, scary, creepy: perceptions of online behavioral advertising. In proceedings of the eighth symposium on usable privacy and security. ACM, 4.

Digital Library

[54]

Weiquan Wang and Izak Benbasat. 2007. Recommendation agents for electronic commerce: Effects of explanation facilities on trusting beliefs. Journal of Management Information Systems 23, 4 (2007), 217--246.

Digital Library

[55]

Jeffrey Warshaw, Tara Matthews, Steve Whittaker, Chris Kau, Mateo Bengualid, and Barton A Smith. 2015. Can an Algorithm Know the Real You?: Understanding People's Reactions to Hyper-personal Analytics Systems. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. ACM, 797--806.

Digital Library

[56]

WhiteHouse. 2016. Big Data: A Report on Algorithmic Systems, Opportunity, and Civil Rights. Washington, DC: Executive Office of the President, White House (2016).

[57]

Craig E Wills and Mihajlo Zeljkovic. 2011. A personalized approach to web privacy: awareness, attitudes and actions. Information Management & Computer Security 19, 1 (2011), 53--73.

[58]

Yaxing Yao, Davide Lo Re, and Yang Wang. 2017. Folk Models of Online Behavioral Advertising. In CSCW. 1957--1969.

Digital Library

Cited By

Kaushik SSharma TYu YAli AWang YZou Y(2024)Cross-Country Examination of People’s Experience with Targeted Advertising on Social MediaExtended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613905.3650780(1-10)Online publication date: 11-May-2024
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Index Terms

Communicating Algorithmic Process in Online Behavioral Advertising
1. Human-centered computing

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cover image ACM Conferences

CHI '18: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

April 2018

8489 pages

ISBN:9781450356206

DOI:10.1145/3173574

General Chairs:
Regan Mandryk
University of Saskatchewan, Canada
,
Mark Hancock
University of Waterloo, Canada
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Program Chairs:
Mark Perry
Brunel University London, UK
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Anna Cox
University College London, UK

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Published: 21 April 2018

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April 21 - 26, 2018

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CHI '18 Paper Acceptance Rate 666 of 2,590 submissions, 26%;

Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

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Cited By

Kaushik SSharma TYu YAli AWang YZou Y(2024)Cross-Country Examination of People’s Experience with Targeted Advertising on Social MediaExtended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613905.3650780(1-10)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613905.3650780
Asthana SIm JChen ZBanovic N(2024)"I know even if you don't tell me": Understanding Users' Privacy Preferences Regarding AI-based Inferences of Sensitive Information for PersonalizationProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642180(1-21)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642180
Moti ZSenol ABostani HBorgesius FMoonsamy VMathur AAcar G(2024)Targeted and Troublesome: Tracking and Advertising on Children’s Websites2024 IEEE Symposium on Security and Privacy (SP)10.1109/SP54263.2024.00118(1517-1535)Online publication date: 19-May-2024
https://doi.org/10.1109/SP54263.2024.00118
Wall EMatzen LEl-Assady MMasters PHosseinpour HEndert ABorgo RChau PPerer ASchupp HStrobelt HPadilla L(2024)Trust Junk and Evil Knobs: Calibrating Trust in AI Visualization2024 IEEE 17th Pacific Visualization Conference (PacificVis)10.1109/PacificVis60374.2024.00012(22-31)Online publication date: 23-Apr-2024
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Sun YDrivas MLiao MSundar S(2023)When Recommender Systems Snoop into Social Media, Users Trust them Less for Health AdviceProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581123(1-14)Online publication date: 19-Apr-2023
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Im JWang RLyu WCook NHabib HCranor LBanovic NSchaub F(2023)Less is Not More: Improving Findability and Actionability of Privacy Controls for Online Behavioral AdvertisingProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580773(1-33)Online publication date: 19-Apr-2023
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