Cited by 215Ethics and Social Impacts of AITechnology's Impact on MediaDigitalization, Law, and Regulation

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Artificial intelligence and mass personalization of communication content -An ethical and literacy perspective

Hermann, Erik·2022·New Media & Society

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Abstract

Artificial intelligence (AI) is (re)shaping communication and contributes to (commercial and informational) need satisfaction by means of mass personalization. However, the substantial personalization and targeting opportunities do not come without ethical challenges. Following an AI-for-social-good perspective, the authors systematically scrutinize the ethical challenges of deploying AI for mass personalization of communication content from a multi-stakeholder perspective. The conceptual analysis reveals interdependencies and tensions between ethical principles, which advocate the need of a basic understanding of AI inputs, functioning, agency, and outcomes. By this form of AI literacy, individuals could be empowered to interact with and treat mass-personalized content in a way that promotes individual and social good while preventing harm.

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Rudin, C. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0048-x · Cited by 10,561
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423
The global landscape of AI ethics guidelines
Jobin, A., Ienca, M., & Vayena, E. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0088-2 · Cited by 6,220 · cited 12×
“The solidarity principle is only featured in 6 out of 84 documents, although it refers to redistributing the benefits of AI to not jeopardize social cohesion (Jobin et al., 2019).”
BackgroundMethodInfluential
AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., et al. (2018) · Minds and Machines · doi:10.1007/s11023-018-9482-5 · Cited by 4,774 · cited 17×
“Notably, personal privacy, accuracy, data protection, and quality are central to the non-maleficence principle (e.g. Floridi et al., 2018; Morley et al., 2020).”
BackgroundMethodInfluential
The ethics of algorithms: Mapping the debate
Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016) · Big Data & Society · doi:10.1177/2053951716679679 · Cited by 4,567 · cited 6×
“Algorithms are the integral components of recommender systems (e.g. Lury and Day, 2019; Milano et al.,2020, 2021; Mittelstadt et al., 2016), that are the foundation of and/ or intensively used by news intermediaries and aggregators, social media, entertainment, and commercial (vendor) platforms…”
BackgroundInfluential
Exposure to ideologically diverse news and opinion on Facebook
Bakshy, E., Messing, S., & Adamic, L. A. (2015) · Science · doi:10.1126/science.aaa1160 · Cited by 3,245
Siri, Siri, in my hand: Who's the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence
Kaplan, A., & Haenlein, M. (2019) · Business Horizons · doi:10.1016/j.bushor.2018.08.004 · Cited by 3,140
Filter Bubbles, Echo Chambers, and Online News Consumption
Flaxman, S., Goel, S., & Rao, J. M. (2016) · Public Opinion Quarterly · doi:10.1093/poq/nfw006 · Cited by 1,992
The Ethics of AI Ethics: An Evaluation of Guidelines
Hagendorff, T. (2020) · Minds and Machines · doi:10.1007/s11023-020-09517-8 · Cited by 1,911 · cited 5×
“To date, the AI ethics landscape is rather fragmented and entails recurring principles (Jobin et al., 2019) that are of high-order, deontological nature (Hagendorff, 2020).”
BackgroundInfluential
The Ethics of AI Ethics -- An Evaluation of Guidelines
Hagendorff, T. (2019) · arXiv · doi:10.48550/arxiv.1903.03425 · Cited by 1,907 · cited 5×
“To date, the AI ethics landscape is rather fragmented and entails recurring principles (Jobin et al., 2019) that are of high-order, deontological nature (Hagendorff, 2020).”
BackgroundInfluential
Principles alone cannot guarantee ethical AI
Mittelstadt, B. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0114-4 · Cited by 1,886 · cited 4×
“practice through mid-level norms and low-level requirements taking into consideration the legal, technical, and social circumstances (Mittelstadt, 2019).”
BackgroundMethodInfluential
Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability
Ananny, M., & Crawford, K. (2018) · New Media & Society · doi:10.1177/1461444816676645 · Cited by 1,836
Thinking critically about and researching algorithms
Kitchin, R. (2016) · Information Communication & Society · doi:10.1080/1369118x.2016.1154087 · Cited by 1,471
Polarization and Partisan Selective Exposure
Stroud, N. J. (2010) · Journal of Communication · doi:10.1111/j.1460-2466.2010.01497.x · Cited by 1,346
Artificial intelligence and communication: A Human–Machine Communication research agenda
Guzman, A. L., & Lewis, S. C. (2019) · New Media & Society · doi:10.1177/1461444819858691 · Cited by 1,238
Selective Exposure in the Age of Social Media: Endorsements Trump Partisan Source Affiliation When Selecting News Online
Messing, S., & Westwood, S. J. (2014) · Communication Research · doi:10.1177/0093650212466406 · Cited by 902
Psychological targeting as an effective approach to digital mass persuasion
Matz, S. C., Kosinski, M., Nave, G., & Stillwell, D. J. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1710966114 · Cited by 892
From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices
Morley, J., Floridi, L., Kinsey, L., & Elhalal, A. (2020) · Science and Engineering Ethics · doi:10.1007/s11948-019-00165-5 · Cited by 882 · cited 12×
“Human agency (i.e. autonomous decisions) and human oversight are focal requirements of autonomy (Morley et al., 2020).”
BackgroundMethodResultInfluential
Rise of Machine Agency: A Framework for Studying the Psychology of Human–AI Interaction (HAII)
Sundar, S. S. (2020) · Journal of Computer-Mediated Communication · doi:10.1093/jcmc/zmz026 · Cited by 871
The like economy: Social buttons and the data-intensive web
Gerlitz, C., & Helmond, A. (2013) · New Media & Society · doi:10.1177/1461444812472322 · Cited by 871
What is Computer Ethics?
MOOR, J. H. (1985) · Metaphilosophy · doi:10.1111/j.1467-9973.1985.tb00173.x · Cited by 863
How AI can be a force for good
Taddeo, M., & Floridi, L. (2018) · Science · doi:10.1126/science.aat5991 · Cited by 760
Ethical Implications and Accountability of Algorithms
Martin, K. (2019) · Journal of Business Ethics · doi:10.1007/s10551-018-3921-3 · Cited by 757
Governing artificial intelligence: ethical, legal and technical opportunities and challenges
Cath, C. (2018) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2018.0080 · Cited by 745 · cited 1×
“Therefore, concrete ethical and socio-legal governance and policies are in demand (Cath, 2018; Theodorou and Dignum, 2020).”
Background
Fair, Transparent, and Accountable Algorithmic Decision-making Processes: The Premise, the Proposed Solutions, and the Open Challenges
Lepri, B., Oliver, N., Letouzé, E., Pentland, A., & Vinck, P. (2018) · Philosophy & Technology · doi:10.1007/s13347-017-0279-x · Cited by 709
Automated Experiments on Ad Privacy Settings: A Tale of Opacity, Choice, and Discrimination
Datta, A., Tschantz, M. C., & Datta, A. (2015) · Proceedings on Privacy Enhancing Technologies · doi:10.1515/popets-2015-0007 · Cited by 676
AI-Mediated Communication: Definition, Research Agenda, and Ethical Considerations
Hancock, J. T., Naaman, M., & Levy, K. (2020) · Journal of Computer-Mediated Communication · doi:10.1093/jcmc/zmz022 · Cited by 658
Bias in algorithmic filtering and personalization
Bozdag, E. (2013) · Ethics and Information Technology · doi:10.1007/s10676-013-9321-6 · Cited by 649
Social Media, News Consumption, and Polarization: Evidence from a Field Experiment
Levy, R. (2021) · American Economic Review · doi:10.1257/aer.20191777 · Cited by 624
Governance by algorithms: reality construction by algorithmic selection on the Internet
Just, N., & Latzer, M. (2016) · Media Culture & Society · doi:10.1177/0163443716643157 · Cited by 616
In AI We Trust: Ethics, Artificial Intelligence, and Reliability
Ryan, M. (2020) · Science and Engineering Ethics · doi:10.1007/s11948-020-00228-y · Cited by 600
Trustworthy artificial intelligence
Thiebes, S., Lins, S., & Sunyaev, A. (2021) · Electronic Markets · doi:10.1007/s12525-020-00441-4 · Cited by 595
Personalization versus Customization: The Importance of Agency, Privacy, and Power Usage
Sundar, S. S., & Marathe, S. S. (2010) · Human Communication Research · doi:10.1111/j.1468-2958.2010.01377.x · Cited by 588
Recommender systems and their ethical challenges
Milano, S., Taddeo, M., & Floridi, L. (2020) · AI & SOCIETY · doi:10.1007/s00146-020-00950-y · Cited by 556
Should we worry about filter bubbles?
Zuiderveen Borgesius, F. J., Trilling, D., Möller, J., Bodó, B., de Vreese, C. H., & Helberger, N. (2016) · Internet Policy Review · doi:10.14763/2016.1.401 · Cited by 541 · cited 2×
“While some studies found evidence that mass personalization induces selective exposure and polarization (e.g. Dylko et al., 2017; Levy, 2021), other studies challenge the assumption and concerns that personalization algorithms necessarily or solely cause filter bubbles, echo chambers, or polarization (Bakshy et al., 2…”
Background
A review on deep learning for recommender systems: challenges and remedies
Batmaz, Z., Yürekli, A., Bilge, A., & Kaleli, C. (2018) · Artificial Intelligence Review · doi:10.1007/s10462-018-9654-y · Cited by 540
Meaningful information and the right to explanation
Selbst, A. D., & Powles, J. (2017) · International Data Privacy Law · doi:10.1093/idpl/ipx022 · Cited by 516
Using Smartphones to Collect Behavioral Data in Psychological Science: Opportunities, Practical Considerations, and Challenges
Harari, G. M., Lane, N. D., Wang, R., Crosier, B. S., Campbell, A. T., & Gosling, S. D. (2016) · Perspectives on Psychological Science · doi:10.1177/1745691616650285 · Cited by 515
Personalized Persuasion
Hirsh, J. B., Kang, S. K., & Bodenhausen, G. V. (2012) · Psychological Science · doi:10.1177/0956797611436349 · Cited by 506
The Psychological Appeal of Personalized Content in Web Portals: Does Customization Affect Attitudes and Behavior?
Kalyanaraman, S., & Sundar, S. S. (2006) · Journal of Communication · doi:10.1111/j.1460-2466.2006.00006.x · Cited by 475
User-generated visibility: Secondary gatekeeping in a shared media space
Singer, J. B. (2013) · New Media & Society · doi:10.1177/1461444813477833 · Cited by 444
The State of Media Literacy
Potter, W. J. (2010) · Journal of Broadcasting & Electronic Media · doi:10.1080/08838151.2011.521462 · Cited by 436
How to Design AI for Social Good: Seven Essential Factors
Floridi, L., Cowls, J., King, T. C., & Taddeo, M. (2020) · Science and Engineering Ethics · doi:10.1007/s11948-020-00213-5 · Cited by 430 · cited 3×
“In light of AI’s impact on the individual, economic, and societal level, the AI ethics literature increasingly focuses ethical frameworks of AI for (social) good (Cowls et al., 2021; Floridi et al.,2018, 2020; Taddeo and Floridi, 2018).”
Background
The ethics of information transparency
Turilli, M., & Floridi, L. (2009) · Ethics and Information Technology · doi:10.1007/s10676-009-9187-9 · Cited by 419
Predicting personality from patterns of behavior collected with smartphones
Stachl, C., Au, Q., Schoedel, R., Gosling, S. D., Harari, G. M., Buschek, D., Völkel, S. T., Schuwerk, T., Oldemeier, M., Ullmann, T., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1920484117 · Cited by 401
Burst of the Filter Bubble?: Effects of personalization on the diversity of Google News
Haim, M., Graefe, A., & Brosius, H.-B. (2018) · Digital Journalism · doi:10.1080/21670811.2017.1338145 · Cited by 399
Establishing the rules for building trustworthy AI
Floridi, L. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0055-y · Cited by 394
The Ethics of AI Ethics: An Evaluation of Guidelines
Hagendorff, T. (2020) · PhilPapers (PhilPapers Foundation) · Cited by 394
Do not blame it on the algorithm: an empirical assessment of multiple recommender systems and their impact on content diversity
Möller, J., Trilling, D., Helberger, N., & van Es, B. (2018) · Information, Communication & Society · doi:10.1080/1369118x.2018.1444076 · Cited by 387
On the Democratic Role of News Recommenders
Helberger, N. (2019) · Digital Journalism · doi:10.1080/21670811.2019.1623700 · Cited by 384
Breaking the filter bubble: democracy and design
Bozdag, E., & van den Hoven, J. (2015) · Ethics and Information Technology · doi:10.1007/s10676-015-9380-y · Cited by 375
Exposure diversity as a design principle for recommender systems
Helberger, N., Karppinen, K., & D’Acunto, L. (2016) · Information Communication & Society · doi:10.1080/1369118x.2016.1271900 · Cited by 368
Some Moral and Technical Consequences of Automation: As machines learn they may develop unforeseen strategies at rates that baffle their programmers.
Wiener, N. (1960) · Science · doi:10.1126/science.131.3410.1355 · Cited by 367 · cited 1×
“The discourse on moral and ethical implications of AI dates back from 1960 (Samuel, 1960; Wiener, 1960).”
Background
A survey of serendipity in recommender systems
Kotkov, D., Wang, S., & Veijalainen, J. (2016) · Knowledge-Based Systems · doi:10.1016/j.knosys.2016.08.014 · Cited by 360
The Ethics of Influence: Government in the Age of Behavioral Science
Sunstein, C. R. (2016) · doi:10.1017/cbo9781316493021 · Cited by 339
Societal and ethical issues of digitization
Royakkers, L., Timmer, J., Kool, L., & van Est, R. (2018) · Ethics and Information Technology · doi:10.1007/s10676-018-9452-x · Cited by 317
Masspersonal communication: A model bridging the mass-interpersonal divide
O'Sullivan, P. B., & Carr, C. T. (2017) · New Media & Society · doi:10.1177/1461444816686104 · Cited by 315
Automation, Journalism, and Human–Machine Communication: Rethinking Roles and Relationships of Humans and Machines in News
Lewis, S. C., Guzman, A. L., & Schmidt, T. R. W. (2019) · Digital Journalism · doi:10.1080/21670811.2019.1577147 · Cited by 290
Why We Need Better Ethics for Emerging Technologies
Moor, J. H. (2005) · Ethics and Information Technology · doi:10.1007/s10676-006-0008-0 · Cited by 278
Personalization of Mass Media and the Growth of Pseudo-Community
Beniger, J. R. (1987) · Communication Research · doi:10.1177/009365087014003005 · Cited by 255
Using a Personality-Profiling Algorithm to Investigate Political Microtargeting: Assessing the Persuasion Effects of Personality-Tailored Ads on Social Media
Zarouali, B., Dobber, T., Pauw, G. D., & de Vreese, C. H. (2020) · Communication Research · doi:10.1177/0093650220961965 · Cited by 249
A framework for the ethical impact assessment of information technology
Wright, D. (2010) · Ethics and Information Technology · doi:10.1007/s10676-010-9242-6 · Cited by 238
What kind of news gatekeepers do we want machines to be? Filter bubbles, fragmentation, and the normative dimensions of algorithmic recommendations
Nechushtai, E., & Lewis, S. C. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2018.07.043 · Cited by 237
A definition, benchmark and database of AI for social good initiatives
Cowls, J., Tsamados, A., Taddeo, M., & Floridi, L. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00296-0 · Cited by 226
THE FUTURE OF PERSONALIZATION AT NEWS WEBSITES
Thurman, N., & Schifferes, S. (2012) · Journalism Studies · doi:10.1080/1461670x.2012.664341 · Cited by 219
Untitled work
Lokot, T. (2016) · doi:10.1080/21670811.2015.1081822 · Cited by 209
Algorithms (and the) everyday
Willson, M. (2016) · Information Communication & Society · doi:10.1080/1369118x.2016.1200645 · Cited by 205
How Do Recommender Systems Affect Sales Diversity? A Cross-Category Investigation via Randomized Field Experiment
Lee, D., & Hosanagar, K. (2017) · Information Systems Research · doi:10.1287/isre.2018.0800 · Cited by 203
Artificial intelligence for human flourishing – Beyond principles for machine learning
Stahl, B. C., Andreou, A., Brey, P., Hatzakis, T., Kirichenko, A., Macnish, K., Laulhé Shaelou, S., Patel, A., Ryan, M., & Wright, D. (2021) · Journal of Business Research · doi:10.1016/j.jbusres.2020.11.030 · Cited by 203
The effects of trait-based personalization in social media advertising
Winter, S., Masłowska, E., & Vos, A. L. (2020) · Computers in Human Behavior · doi:10.1016/j.chb.2020.106525 · Cited by 201
Using Big Data as a window into consumers’ psychology
Matz, S., & Netzer, O. (2017) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2017.05.009 · Cited by 182
Personality Research and Assessment in the Era of Machine Learning
Stachl, C., Pargent, F., Hilbert, S., Harari, G. M., Schoedel, R., Vaid, S. S., Gosling, S. D., & Bühner, M. (2020) · European Journal of Personality · doi:10.1002/per.2257 · Cited by 178
Behavioural evidence for a transparency–efficiency tradeoff in human–machine cooperation
Ishowo-Oloko, F., Bonnefon, J.-F., Soroye, Z., Crandall, J., Rahwan, I., & Rahwan, T. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0113-5 · Cited by 178
How social network sites and other online intermediaries increase exposure to news
Scharkow, M., Mangold, F., Stier, S., & Breuer, J. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1918279117 · Cited by 176
Selling News to Audiences – A Qualitative Inquiry into the Emerging Logics of Algorithmic News Personalization in European Quality News Media
Bodó, B. (2019) · Digital Journalism · doi:10.1080/21670811.2019.1624185 · Cited by 175
What an Algorithm Is
Hill, R. K. (2015) · Philosophy & Technology · doi:10.1007/s13347-014-0184-5 · Cited by 161
The dark side of technology: An experimental investigation of the influence of customizability technology on online political selective exposure
Dylko, I. B., Dolgov, I., Hoffman, W. B., Eckhart, N. R., Molina, M. D., & Aaziz, O. (2017) · Computers in Human Behavior · doi:10.1016/j.chb.2017.03.031 · Cited by 158
Algorithmic Personalization as a Mode of Individuation
Lury, C., & Day, S. (2019) · Theory Culture & Society · doi:10.1177/0263276418818888 · Cited by 157
Towards ethical and socio-legal governance in AI
Theodorou, A., & Dignum, V. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-019-0136-y · Cited by 156
An embedded ethics approach for AI development
McLennan, S., Fiske, A., Celi, L. A., Müller, R., Harder, J., Ritt, K., Haddadin, S., & Buyx, A. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0214-1 · Cited by 150
Algorithms, Automation, and News
Thurman, N., Lewis, S. C., & Kunert, J. (2019) · Digital Journalism · doi:10.1080/21670811.2019.1685395 · Cited by 149
Big data analytics and the limits of privacy self-management
Baruh, L., & Popescu, M. (2015) · New Media & Society · doi:10.1177/1461444815614001 · Cited by 147
Interested in Diversity: The role of user attitudes, algorithmic feedback loops, and policy in news personalization
Bodó, B., Helberger, N., Eskens, S., & Möller, J. (2019) · Digital Journalism · doi:10.1080/21670811.2018.1521292 · Cited by 141
Privacy in the age of psychological targeting
Matz, S., Appel, R. E., & Kosiński, M. (2019) · Current Opinion in Psychology · doi:10.1016/j.copsyc.2019.08.010 · Cited by 137
The rise of the social algorithm
Lazer, D. (2015) · Science · doi:10.1126/science.aab1422 · Cited by 133
Human-aligned artificial intelligence is a multiobjective problem
Vamplew, P. W., Dazeley, R., Foale, C., Firmin, S., & Mummery, J. (2017) · Ethics and Information Technology · doi:10.1007/s10676-017-9440-6 · Cited by 129
Anticipating ethical issues in emerging IT
Brey, P. A. E. (2012) · Ethics and Information Technology · doi:10.1007/s10676-012-9293-y · Cited by 128
Curation Algorithms and Filter Bubbles in Social Networks
Berman, R., & Katona, Z. (2020) · Marketing Science · doi:10.1287/mksc.2019.1208 · Cited by 124
Algorithm Overdependence: How the Use of Algorithmic Recommendation Systems Can Increase Risks to Consumer Well-Being
Banker, S., & Khetani, S. (2019) · Journal of Public Policy & Marketing · doi:10.1177/0743915619858057 · Cited by 120
Algorithms (and the) everyday
Willson, M. A. (2019) · Routledge eBooks · doi:10.4324/9781351200677-9 · Cited by 115
The personalization–privacy paradox in the attention economy
Cloarec, J. (2020) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2020.120299 · Cited by 112
Automated Serendipity
Fletcher, R., & Nielsen, R. K. (2018) · Digital Journalism · doi:10.1080/21670811.2018.1502045 · Cited by 106
Serendipity as an emerging design principle of the infosphere: challenges and opportunities
Reviglio, U. (2019) · Ethics and Information Technology · doi:10.1007/s10676-018-9496-y · Cited by 105
Algorithms as discrimination detectors
Kleinberg, J. M., Ludwig, J., Mullainathan, S., & Sunstein, C. R. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1912790117 · Cited by 99
Some Moral and Technical Consequences of Automation—A Refutation
Samuel, A. L. (1960) · Science · doi:10.1126/science.132.3429.741 · Cited by 98
Mass personalization: Predictive marketing algorithms and the reshaping of consumer knowledge
Kotras, B. (2020) · Big Data & Society · doi:10.1177/2053951720951581 · Cited by 95
Media as Data Extraction: Towards a New Map of a Transformed Communications Field
Turow, J., & Couldry, N. (2018) · Journal of Communication · doi:10.1093/joc/jqx011 · Cited by 94
Lessons for artificial intelligence from the study of natural stupidity
Rich, A. S., & Gureckis, T. M. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0038-z · Cited by 94
Algorithmic Personalization and the Two-Step Flow of Communication
Soffer, O. (2019) · Communication Theory · doi:10.1093/ct/qtz008 · Cited by 91
Human Still in the Loop
Milosavljević, M., & Vobič, I. (2019) · Digital Journalism · doi:10.1080/21670811.2019.1601576 · Cited by 90
Tolerant Paternalism: Pro-ethical Design as a Resolution of the Dilemma of Toleration
Floridi, L. (2015) · Science and Engineering Ethics · doi:10.1007/s11948-015-9733-2 · Cited by 87
Vectors into the Future of Mass and Interpersonal Communication Research: Big Data, Social Media, and Computational Social Science
Cappella, J. N. (2017) · Human Communication Research · doi:10.1111/hcre.12114 · Cited by 83
Ethical aspects of multi-stakeholder recommendation systems
Milano, S., Taddeo, M., & Floridi, L. (2020) · The Information Society · doi:10.1080/01972243.2020.1832636 · Cited by 81
Predicting the Personal Appeal of Marketing Images Using Computational Methods
Matz, S., Segalin, C., Stillwell, D. J., Müller, S. R., & Bos, M. W. (2019) · Journal of Consumer Psychology · doi:10.1002/jcpy.1092 · Cited by 75
Method in computer ethics: Towards a multi-level interdisciplinary approach
Brey, P. A. E. (2000) · Ethics and Information Technology · doi:10.1023/a:1010076000182 · Cited by 72
Newsbots That Mediate Journalist and Audience Relationships
Ford, H., & Hutchinson, J. P. (2019) · Digital Journalism · doi:10.1080/21670811.2019.1626752 · Cited by 71
When Self Is the Source: Effects of Media Customization on Message Processing
Kang, H., & Sundar, S. S. (2016) · Media Psychology · doi:10.1080/15213269.2015.1121829 · Cited by 62
Vulnerability in a tracked society: Combining tracking and survey data to understand who gets targeted with what content
Bol, N., Strycharz, J., Helberger, N., van de Velde, B., & de Vreese, C. H. (2020) · New Media & Society · doi:10.1177/1461444820924631 · Cited by 57
The Form of Content Personalisation at Mainstream, Transatlantic News Outlets: 2010–2016
Kunert, J., & Thurman, N. (2019) · Journalism Practice · doi:10.1080/17512786.2019.1567271 · Cited by 41
Solidarity should be a core ethical principle of AI
Luengo-Oroz, M. A. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0115-3 · Cited by 37
Techno-capital: Theorizing media and information literacy through information technology capabilities
Choi, J. R., Straubhaar, J. D., Skouras, M., Park, S., Santillana, M., & Strover, S. (2020) · New Media & Society · doi:10.1177/1461444820925800 · Cited by 34
Prioritizing the Audience’s View of Automation in Journalism
Guzman, A. L. (2019) · Digital Journalism · doi:10.1080/21670811.2019.1681902 · Cited by 33
Facebook use as a communicative relation: exploring the relation between Facebook users and the algorithmic news feed
Schwartz, S., & Mahnke, M. (2020) · Information, Communication & Society · doi:10.1080/1369118x.2020.1718179 · Cited by 31
On conflicts between ethical and logical principles in artificial intelligence
D’Acquisto, G. (2020) · AI & Society · doi:10.1007/s00146-019-00927-6 · Cited by 19
The tracked society: Interdisciplinary approaches on online tracking
König, R., Uphues, S., Vogt, V., & Kolany-Raiser, B. (2020) · New Media & Society · doi:10.1177/1461444820924629 · Cited by 7
Komunikasi Pemasaran Terpadu Dalam Pengembangan Pariwisata Ende Studi Kasus: Festival Danau Kelimutu
Mola, M. S. R., & Witarti, D. I. (2020) · Communicatio · doi:10.36080/comm.v11i1.1004 · Cited by 6

Research works printed but not linked: 2

Gunkel DJ, 2012, Communication+, 11, 1
Thurman N, 2019, Digital Journalism, 7, 447

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See all 215

Authors: Hermann, E.
Affiliations: Leibniz Institute for High Performance Microelectronics (DE)
Corresponding author: Hermann, Erik
ORCID: Hermann, Erik
Venue: New Media & Society
Publisher: SAGE Publications
Published: 2021-06-18
Volume: 24
Issue: 5
Pages: 1258-1277
ISSN: 1461-4448 (ISSN-L)
1461-7315
DOI: 10.1177/14614448211022702
OpenAlex: W3174208514
Cited by: 214
References: 116
Citations by year: 2026: 38 · 2025: 75 · 2024: 56 · 2023: 29 · 2022: 13 · 2021: 3
FWCI: 31.57
Type: journal article
Language: en
Open access: closed
Licence: journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
Locations: New Media & Society (publishedVersion)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
Social Sciences › Communication › Technology's Impact on Media
Social Sciences › Law › Digitalization, Law, and Regulation
Keywords: artificial intelligence, mass personalization, communication content, AI ethics, AI literacy, AI for social good, multi-stakeholder perspectives, targeted communication, need satisfaction, agency, harm prevention

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Artificial intelligence and mass personalization of communication content -An ethical and literacy perspective

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