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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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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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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