Communicating Algorithmic Process in Online Behavioral Advertising

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.

[1]  Karrie Karahalios,et al.  First I "like" it, then I hide it: Folk Theories of Social Feeds , 2016, CHI.

[2]  René F. Kizilcec.  How Much Information?: Effects of Transparency on Trust in an Algorithmic Interface , 2016, CHI.

[3]  Krishna P. Gummadi,et al.  Investigating Ad Transparency Mechanisms in Social Media: A Case Study of Facebooks Explanations , 2018, NDSS.

[4]  Emilee J. Rader,et al.  Awareness of Behavioral Tracking and Information Privacy Concern in Facebook and Google , 2014, SOUPS.

[5]  Rashmi R. Sinha,et al.  The role of transparency in recommender systems , 2002, CHI Extended Abstracts.

[6]  Nicholas Diakopoulos,et al.  Algorithmic Transparency in the News Media , 2017 .

[7]  Erik Wästlund,et al.  Usable Transparency with the Data Track: A Tool for Visualizing Data Disclosures , 2015, CHI Extended Abstracts.

[8]  Darren M. Stevenson.  Data, Trust, and Transparency in Personalized Advertising. , 2016 .

[9]  Gerhard Friedrich,et al.  A Taxonomy for Generating Explanations in Recommender Systems , 2011, AI Mag..

[10]  Blase Ur,et al.  What do online behavioral advertising privacy disclosures communicate to users? , 2012, WPES '12.

[11]  Norman M. Sadeh,et al.  What do they know about me? Contents and Concerns of Online Behavioral Profiles , 2015, ArXiv.

[12]  Ramesh Govindan,et al.  AdReveal: improving transparency into online targeted advertising , 2013, HotNets.

[13]  Seth Flaxman,et al.  EU regulations on algorithmic decision-making and a "right to explanation" , 2016, ArXiv.

[14]  J. Turow.  The Aisles Have Eyes: How Retailers Track Your Shopping, Strip Your Privacy, and Define Your Power , 2017 .

[15]  N. Diakopoulos.  Algorithmic Accountability Reporting: On the Investigation of Black Boxes , 2014 .

[16]  Kyle Kubler.  The Black Box Society: the secret algorithms that control money and information , 2016 .

[17]  Claude Castelluccia,et al.  MyTrackingChoices: Pacifying the Ad-Block War by Enforcing User Privacy Preferences , 2016, ArXiv.

[18]  Weng-Keen Wong,et al.  Too much, too little, or just right? Ways explanations impact end users' mental models , 2013, 2013 IEEE Symposium on Visual Languages and Human Centric Computing.

[19]  Nicholas Diakopoulos,et al.  Algorithmic Accountability , 2015 .

[20]  Wai-Tat Fu,et al.  You Want Me to Work with Who?: Stakeholder Perceptions of Automated Team Formation in Project-based Courses , 2017, CHI.

[21]  Taeyoung Lee,et al.  Us vs. Them: Understanding Artificial Intelligence Technophobia over the Google DeepMind Challenge Match , 2017, CHI.

[22]  K. Foot,et al.  Media Technologies: Essays on Communication, Materiality, and Society , 2014 .

[23]  H. Beales,et al.  The Value of Behavioral Targeting , 2010 .

[24]  Karrie Karahalios,et al.  Auditing Algorithms : Research Methods for Detecting Discrimination on Internet Platforms , 2014 .

[25]  Doc Searls,et al.  The Intention Economy: When Customers Take Charge , 2012 .

[26]  J. Söderberg.  Media Technologies - Essays on Communication, Materiality, and Society , 2014 .

[27]  Martina Gillen,et al.  Internet Co-Regulation: European Law, Regulatory Governance and Legitimacy in Cyberspace , 2012, Int. J. Law Inf. Technol..

[28]  John Cheney-Lippold,et al.  A New Algorithmic Identity , 2011 .

[29]  Blase Ur,et al.  Watching Them Watching Me: Browser Extensions Impact on User Privacy Awareness and Concern , 2016 .

[30]  John Riedl,et al.  Explaining collaborative filtering recommendations , 2000, CSCW '00.

[31]  Phoebe Sengers,et al.  The Case of the Strangerationist: Re-interpreting Critical Technical Practice , 2016, Conference on Designing Interactive Systems.

[32]  Steve Whittaker,et al.  Dice in the Black Box: User Experiences with an Inscrutable Algorithm , 2018, AAAI Spring Symposia.

[33]  Tara Matthews,et al.  Can an Algorithm Know the "Real You"?: Understanding People's Reactions to Hyper-personal Analytics Systems , 2015, CHI.

[34]  Helen Nissenbaum,et al.  Privacy in Context - Technology, Policy, and the Integrity of Social Life , 2009 .

[35]  Li Chen,et al.  Trust-inspiring explanation interfaces for recommender systems , 2007, Knowl. Based Syst..

[36]  Pablo J. Boczkowski,et al.  The Relevance of Algorithms , 2013 .

[37]  Matthew Crain,et al.  The limits of transparency: Data brokers and commodification , 2018, New Media Soc..

[38]  Dennis R. Wixon,et al.  CHI '02 Extended Abstracts on Human Factors in Computing Systems , 2002, CHI 2002.

[39]  Jules Polonetsky,et al.  To Track or 'Do Not Track': Advancing Transparency and Individual Control in Online Behavioral Advertising , 2011 .

[40]  Yang Wang,et al.  Smart, useful, scary, creepy: perceptions of online behavioral advertising , 2012, SOUPS.

[41]  Yang Wang,et al.  Folk Models of Online Behavioral Advertising , 2017, CSCW.

[42]  Izak Benbasat,et al.  Recommendation Agents for Electronic Commerce: Effects of Explanation Facilities on Trusting Beliefs , 2007, J. Manag. Inf. Syst..

[43]  Lawrence M. Friedman.  The Horizontal Society , 1999 .

[44]  Claude Castelluccia,et al.  MyAdChoices: Bringing Transparency and Control to Online Advertising , 2016, ACM Trans. Web.

[45]  Karrie Karahalios,et al.  "Be Careful; Things Can Be Worse than They Appear": Understanding Biased Algorithms and Users' Behavior Around Them in Rating Platforms , 2017, ICWSM.

[46]  Craig E. Wills,et al.  A Personalized Approach to Web Privacy - Awareness, Attitudes and Actions , 2011, Inf. Manag. Comput. Secur..

[47]  M. Chalmers,et al.  Seamful and Seamless Design in Ubiquitous Computing , 2003 .