Privacy in the age of psychological targeting.

Psychological targeting describes the practice of extracting people's psychological profiles from their digital footprints (e.g. their Facebook Likes, Tweets or credit card records) in order to influence their attitudes, emotions or behaviors through psychologically informed interventions at scale. We discuss how the increasingly blurred lines between public and private information, and the continuation of the outdated practices of notice and consent, challenge traditional conceptualizations of privacy in the context of psychological targeting. Drawing on the theory of contextual integrity, we argue that it is time to rethink privacy and move beyond the questions of who collects what data to how the data are being used. Finally, we suggest that regulations of psychological targeting should be accompanied by a mindset that fosters (1) privacy by design to make it easy for individuals to act in line with their privacy goals, as well as (2) disclosure by choice, to allow individuals to freely decide whether and when they might be willing to forsake their privacy for better service.

[1]  Francesca Pratesi,et al.  Privacy-by-design in big data analytics and social mining , 2014, EPJ Data Science.

[2]  David Stillwell,et al.  Money Buys Happiness When Spending Fits Our Personality , 2016, Psychological science.

[3]  Barry Schwartz,et al.  Doing Better but Feeling Worse: The Paradox of Choice , 2012 .

[4]  Gregory J. Park,et al.  Automatic personality assessment through social media language. , 2015, Journal of personality and social psychology.

[5]  Tal Yarkoni Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers. , 2010, Journal of research in personality.

[6]  N. Lane,et al.  MoodScope: building a mood sensor from smartphone usage patterns , 2013, MobiSys '13.

[7]  Daniel J. Solove,et al.  Privacy Self-Management and the Consent Dilemma , 2012 .

[8]  M. Kosinski,et al.  Deep Neural Networks Are More Accurate Than Humans at Detecting Sexual Orientation From Facial Images , 2018, Journal of personality and social psychology.

[9]  Sandra C. Matz,et al.  Can Psychological Traits Be Inferred From Spending? Evidence From Transaction Data , 2019, Psychological science.

[10]  F. Wahle,et al.  Mobile Sensing and Support for People With Depression: A Pilot Trial in the Wild , 2016, JMIR mHealth and uHealth.

[11]  Bernd Marcus,et al.  Personality in cyberspace: personal Web sites as media for personality expressions and impressions. , 2006, Journal of personality and social psychology.

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

[13]  S. Gosling,et al.  Facebook as a research tool for the social sciences: Opportunities, challenges, ethical considerations, and practical guidelines. , 2015, The American psychologist.

[14]  B. Rimer,et al.  Advancing Tailored Health Communication: A Persuasion and Message Effects Perspective , 2006 .

[15]  Andrew D. Selbst,et al.  Big Data's Disparate Impact , 2016 .

[16]  Bamshad Mobasher,et al.  Emotions in Context-Aware Recommender Systems , 2017, Emotions and Personality in Personalized Services.

[17]  David C. Mohr,et al.  Ecological momentary interventions for depression and anxiety , 2017, Depression and anxiety.

[18]  Alessandro Perina,et al.  The Pictures We Like Are Our Image: Continuous Mapping of Favorite Pictures into Self-Assessed and Attributed Personality Traits , 2017, IEEE Transactions on Affective Computing.

[19]  Helen Nissenbaum,et al.  Contextual Integrity Up and Down the Data Food Chain , 2019, Theoretical Inquiries in Law.

[20]  Helen Nissenbaum,et al.  Big data's end run around procedural privacy protections , 2014, Commun. ACM.

[21]  T. Graepel,et al.  Private traits and attributes are predictable from digital records of human behavior , 2013, Proceedings of the National Academy of Sciences.

[22]  G. Bodenhausen,et al.  Personalized Persuasion , 2012, Psychological science.

[23]  S. Iyengar,et al.  The Dark Side of Choice: When Choice Impairs Social Welfare , 2006 .

[24]  Danny Azucar,et al.  Predicting the Big 5 personality traits from digital footprints on social media: A meta-analysis , 2018 .

[25]  G. Loewenstein,et al.  Privacy and human behavior in the age of information , 2015, Science.

[26]  Youngme Moon,et al.  Personalization and Personality: Some Effects of Customizing Message Style Based on Consumer Personality , 2002 .

[27]  D. Ben-Zeev,et al.  Feasibility, acceptability, and preliminary efficacy of a smartphone intervention for schizophrenia. , 2014, Schizophrenia bulletin.

[28]  Daniele Quercia,et al.  Our Twitter Profiles, Our Selves: Predicting Personality with Twitter , 2011, 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing.

[29]  Margaret L. Kern,et al.  Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach , 2013, PloS one.

[30]  Ambuj Tewari,et al.  Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support , 2017, Annals of behavioral medicine : a publication of the Society of Behavioral Medicine.

[31]  Ileana-Gabriela Pintiliuc Protection of personal data , 2018, Logos Universality Mentality Education Novelty: Law.

[32]  Lei Zheng,et al.  DeepMood: Modeling Mobile Phone Typing Dynamics for Mood Detection , 2017, KDD.

[33]  John R. Hauser,et al.  Website Morphing , 2009, Mark. Sci..

[34]  M. Kosinski,et al.  Psychological targeting as an effective approach to digital mass persuasion , 2017, Proceedings of the National Academy of Sciences.

[35]  S. De Geest,et al.  Adherence to Long-Term Therapies: Evidence for Action , 2003, European journal of cardiovascular nursing : journal of the Working Group on Cardiovascular Nursing of the European Society of Cardiology.

[36]  W. Price,et al.  Privacy in the age of medical big data , 2019, Nature Medicine.

[37]  Mohammad Mahdi Ghassemi,et al.  Predicting Latent Narrative Mood Using Audio and Physiologic Data , 2017, AAAI.

[38]  Tiffany Ya Tang,et al.  The role of user mood in movie recommendations , 2010, Expert Syst. Appl..

[39]  Peter Salovey,et al.  Motivating Cancer Prevention and Early Detection Behaviors using Psychologically Tailored Messages , 2005, Journal of health communication.

[40]  M. Kosinski,et al.  Computer-based personality judgments are more accurate than those made by humans , 2015, Proceedings of the National Academy of Sciences.

[41]  Daniel J. Solove,et al.  Introduction: Privacy Self-Management and the Consent Dilemma , 2013 .

[42]  S. Athey,et al.  The Digital Privacy Paradox: Small Money, Small Costs, Small Talk , 2017 .

[43]  Fred H. Cate,et al.  Notice and consent in a world of Big Data , 2013 .

[44]  Helen Nissenbaum,et al.  On Notice: The Trouble with Notice and Consent , 2009 .

[45]  R. Pieters,et al.  Emotion-Induced Engagement in Internet Video Advertisements , 2012 .

[46]  Sung-Bae Cho,et al.  A Context-Aware Music Recommendation System Using Fuzzy Bayesian Networks with Utility Theory , 2006, FSKD.