The DOI matches its Crossref record; 116 of the 116 references it cites were found in a registry or an index.
Artificial intelligence and mass personalization of communication content -An ethical and literacy perspective
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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“Even though AI education represents an urgent goal to be pursued in the context of combating disinformation, the delay in dissemination programmes stems from the ongoing debate on what constitutes relevant AI knowledge (Hermann, 2022;Kandlhofer & Steinbauer, 2018;Long & Magerko, 2020;Mikalef & Gupta, 2021):”
From Deepfakes, Democracy, and the Ethics of Synthetic Media: A Synthesis of the SOLARIS Project (2026) · cites this paper as Hermann, 2022
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Cites
Rudin, C. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0048-x · Cited by 10,561
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).”
BackgroundMethodInfluentialFloridi, 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).”
BackgroundMethodInfluentialThe 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…”
BackgroundInfluentialExposure 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
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).”
BackgroundInfluentialThe 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
Research works printed but not linked: 2
- Gunkel DJ, 2012, Communication+, 11, 1
- Thurman N, 2019, Digital Journalism, 7, 447
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Reis, L. K., & Schütz, P. (2026) · MedienPädagogik Zeitschrift für Theorie und Praxis der Medienbildung · doi:10.21240/mpaed/71/2026.08.05.x
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Metadata
- 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
- 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
- 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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