Al Ethics in Digital Media Ecosystems: Balancing Algorithmic Efficiency and Human Protection

Al Ethics in Digital Media Ecosystems: Balancing Algorithmic Efficiency and Human Protection

Authors

  • Sumaiyah Maya Universitas Muhammadiyah Riau
  • Nolly Media Putra University of Muhammadiyah Riau
  • Laura Zevira Arfa Universitas Muhammadiyah Riau

Keywords:

Ethics of Artificial Intelligence; Digital Ecosystem; Digital Media and Communication; Algorithms and Human Protection

Abstract

The development of artificial intelligence (AI) in the digital media and communication ecosystem has brought a leap in efficiency through the automation of content production, personalization of information distribution and optimization of audience engagement. However, behind the efficiency of the algorithm arise various ethical issues related to privacy, bias, polarization, manipulation and protection of vulnerable groups. This study aims to critically understand the ethics of artificial intelligence (Al) in the media and digital communication ecosystem by highlighting the tension between algorithmic efficiency logic and the need for human protection. This study uses a qualitative approach by combining interviews and systematic literature review. In-depth interviews with key informants consisting of media practitioners, regulators and academics to explore empirical experiences and normative views related to the use of A/ in the media. Meanwhile, literature reviews are conducted on journal articles, academic books and policy documents discussing ethics in the media and digital communication sectors. The thematic analysis was carried out by mapping the practice of using Al in three main domains, namely journalism, social media and video/digital advertising platforms. The results show that the application of Al in the media tends to be driven by efficiency and commercialization, while ethical dimensions such as justice, transparency and accountability and respect for human dignity are often not adequately integrated in the design and governance of algorithms. This article proposes an ethical responsibility framework that places media companies, digital platforms, AL developers, regulators and users as actors in the development of human centered and responsible AL practices in the media sector, especially in the context of digital transformation in developing countries such as Indonesia.

Downloads

Download data is not yet available.

References

Amy, R. A. (2024). Public attitudes towards the use of AI in journalism | Reuters Institute for the Study of Journalism. http://reutersinstitute.politics.ox.ac.uk/digital-news-report/2024/public-attitudes-towards-use-ai-and-journalism

Ananny, M., & Crawford, K. (2018). Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability. New Media & Society, 20(3), 973–989. https://doi.org/10.1177/1461444816676645

Ariansyah, K., Barsei, A. N., Syahr, Z. H. A., Sipahutar, N. Y. P., Damanik, M. P., Perdananugraha, G. M., Dunan, A., Nupikso, D., Darmanto, Hidayat, D., Mudjiyanto, B., Hermawati, I., & Suryanegara, M. (2023). Unleashing the potential of mobile broadband: Evidence from Indonesia's underdeveloped regions on its role in reducing income inequality. Telematics and Informatics, 82, 102012. https://doi.org/10.1016/j.tele.2023.102012

Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? . Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–623. https://doi.org/10.1145/3442188.3445922

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa

Broussard, M., Diakopoulos, N., Guzman, A. L., Abebe, R., Dupagne, M., & Chuan, C.-H. (2019). Artificial Intelligence and Journalism. Journalism & Mass Communication Quarterly, 96(3), 673–695. https://doi.org/10.1177/1077699019859901

Buolamwini, J., & Gebru, T. (2018). Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification.

Burrell, J. (2016). How the machine 'thinks': Understanding opacity in machine learning algorithms. Big Data & Society, 3(1), 2053951715622512. https://doi.org/10.1177/2053951715622512

Calvo-Rubio, L.-M., & Ufarte-Ruiz, M.-J. (2021). Artificial intelligence and journalism: Systematic review of scientific production in Web of Science and Scopus (2008-2019). Communication & Society, 159–176. https://doi.org/10.15581/003.34.2.159-176

Cath, C. (2018). Governing artificial intelligence: Ethical, legal and technical opportunities and challenges. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2133), 20180080. https://doi.org/10.1098/rsta.2018.0080

Cinelli, M., De Francisci Morales, G., Galeazzi, A., Quattrociocchi, W., & Starnini, M. (2021). The echo chamber effect on social media. Proceedings of the National Academy of Sciences, 118(9), e2023301118. https://doi.org/10.1073/pnas.2023301118

Creswell, J. W., & J. David. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches.

Danzon-Chambaud, S. (2021). A systematic review of automated journalism scholarship: Guidelines and suggestions for future research. Open Research Europe, 1, 4. https://doi.org/10.12688/openreseurope.13096.1

Del Vicario, M., Bessi, A., Zollo, F., Petroni, F., Scala, A., Caldarelli, G., Stanley, H. E., & Quattrociocchi, W. (2016). The spread of misinformation online. Proceedings of the National Academy of Sciences, 113(3), 554–559. https://doi.org/10.1073/pnas.1517441113

Diakopoulos, N. (2015). Algorithmic Accountability: Journalistic investigation of computational power structures. Digital Journalism, 3(3), 398–415. https://doi.org/10.1080/21670811.2014.976411

DiCicco‐Bloom, B., & Crabtree, B. F. (2006). The qualitative research interview. Medical Education, 40(4), 314–321. https://doi.org/10.1111/j.1365-2929.2006.02418.x

Dorr, K. N. (2016). Mapping the field of Algorithmic Journalism. Digital Journalism, 4(6), 700–722. https://doi.org/10.1080/21670811.2015.1096748

Flaxman, S., Goel, S., & Rao, J. M. (2016). Filter Bubbles, Echo Chambers, and Online News Consumption. Public Opinion Quarterly, 80(S1), 298–320. https://doi.org/10.1093/poq/nfw006

Floridi, L., & Cowls, J. (2019a). A Unified Framework of Five Principles for AI in Society. Harvard Data Science Review. https://doi.org/10.1162/99608f92.8cd550d1

Floridi, L., & Cowls, J. (2019b). A Unified Framework of Five Principles for AI in Society. Harvard Data Science Review. https://doi.org/10.1162/99608f92.8cd550d1

Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & Vayena, E. (2018). AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5

Gorwa, R., Binns, R., & Katzenbach, C. (2020). Algorithmic content moderation: Technical and political challenges in the automation of platform governance. Big Data & Society, 7(1), 205395171989794. https://doi.org/10.1177/2053951719897945

Gutierrez Caneda, B., Lindén, C.-G., & Vázquez-Herrero, J. (2024). Ethics and journalistic challenges in the age of artificial intelligence: Talking with professionals and experts. Frontiers in Communication, 9, 1465178. https://doi.org/10.3389/fcomm.2024.1465178

Guzman, A. L., & Lewis, S. C. (2020). Artificial intelligence and communication: A Human–Machine Communication research agenda. New Media & Society, 22(1), 70–86. https://doi.org/10.1177/1461444819858691

Helberger, N. (2019). On the Democratic Role of News Recommenders. Digital Journalism, 7(8), 993–1012. https://doi.org/10.1080/21670811.2019.1623700

Hofeditz, L., Jung, A.-K., Mirbabaie, M., & Stieglitz, S. (2025). Ethical Guidelines for the Application of Generative AI in German Journalism. Digital Society, 4(1), 4. https://doi.org/10.1007/s44206-024-00151-w

Jess Weatherbed. (2025). The New York Times adopts AI tools in the newsroom | The Verge. https://www.theverge.com/news/613989/new-york-times-internal-ai-tools-echo?utm_source=chatgpt.com

Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399. https://doi.org/10.1038/s42256-019-0088-2

Kallio, H., Pietilä, A., Johnson, M., & Kangasniemi, M. (2016). Systematic methodological review: Developing a framework for a qualitative semi‐structured interview guide. Journal of Advanced Nursing, 72(12), 2954–2965. https://doi.org/10.1111/jan.13031

Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who's the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15–25. https://doi.org/10.1016/j.bushor.2018.08.004

Lemonne, E. (2018, December 17). Ethics Guidelines for Trustworthy AI [Text]. FUTURIUM - European Commission. https://ec.europa.eu/futurium/en/ai-alliance-consultation

Sigh. (2021). Media Control in the Digital Politics of Indonesia. Media and Communication, 9(4), 52–61. https://doi.org/10.17645/mac.v9i4.4225

Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2022). A Survey on Bias and Fairness in Machine Learning. ACM Computing Surveys, 54(6), 1–35. https://doi.org/10.1145/3457607

Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019). Model Cards for Model Reporting. Proceedings of the Conference on Fairness, Accountability, and Transparency, 220–229. https://doi.org/10.1145/3287560.3287596

Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 2053951716679679. https://doi.org/10.1177/2053951716679679

Molla, M. A. M., & Ahsan, M. M. (2025). Artificial intelligence and journalism: A systematic bibliometric and thematic analysis of global research. Computers in Human Behavior Reports, 20, 100830. https://doi.org/10.1016/j.chbr.2025.100830

Moller, J., Trilling, D., Helberger, N., & Van Es, B. (2018). Do not blame it on the algorithm: An empirical assessment of multiple recommender systems and their impact on content diversity. Information, Communication & Society, 21(7), 959–977. https://doi.org/10.1080/1369118X.2018.1444076

Nieborg, D. B., & Poell, T. (2018). The platformization of cultural production: Theorizing the contingent cultural commodity. New Media & Society, 20(11), 4275–4292. https://doi.org/10.1177/1461444818769694

Peter Brown and Klaudia, & Jazwinnska. (2025). Journalism Zero: How Platforms and Publishers are Navigating AI.

Porlezza, C., & Schapals, A. K. (2024a). AI Ethics in Journalism (Studies): An Evolving Field Between Research and Practice. Emerging Media, 2(3), 356–370. https://doi.org/10.1177/27523543241288818

Porlezza, C., & Schapals, A. K. (2024b). AI Ethics in Journalism (Studies): An Evolving Field Between Research and Practice. Emerging Media, 2(3), 356–370. https://doi.org/10.1177/27523543241288818

Regulation (EU) (2024). http://data.europa.eu/eli/reg/2024/1689/oj

Runhe Gu. (2025). Algorithmic Bias and Information Diversity: Formation Mechanism and Countermeasure Path of Filter Bubbles in Social Media. Lecture Notes in Education Psychology and Public Media, 114(1), 99–104. https://doi.org/10.54254/2753-7048/2025.KM26569

Sayedi, A. (2018). Real-Time Bidding in Online Display Advertising. Marketing Science, 37(4), 553–568. https://doi.org/10.1287/mksc.2017.1083

Shneiderman, B. (2020a). Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy. International Journal of Human–Computer Interaction, 36(6), 495–504. https://doi.org/10.1080/10447318.2020.1741118

Shneiderman, B. (2020b). Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy. International Journal of Human–Computer Interaction, 36(6), 495–504. https://doi.org/10.1080/10447318.2020.1741118

Snyder, H. (2019a). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339. https://doi.org/10.1016/j.jbusres.2019.07.039

Snyder, H. (2019b). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339. https://doi.org/10.1016/j.jbusres.2019.07.039

Sonni, A. F., Putri, V. C. C., & Irwanto, I. (2024). Bibliometric and Content Analysis of the Scientific Work on Artificial Intelligence in Journalism. Journalism and Media, 5(2), 787–798. https://doi.org/10.3390/journalmedia5020051

Tabassi, E. (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0) (NIST AI 100-1 No. National Institute of Standards and Technology (U.S.). https://doi.org/10.6028/NIST.AI.100-1

Tapsell, R. (2015). Platform convergence in Indonesia: Challenges and opportunities for media freedom. Convergence: The International Journal of Research into New Media Technologies, 21(2), 182–197. https://doi.org/10.1177/1354856514531527

The White House. (2022). Blueprint for an AI Bill of Rights | OSTP. The White House. https://bidenwhitehouse.archives.gov/ostp/ai-bill-of-rights/

UNESCO. (2024). Recommendation on the Ethics of Artificial Intelligence | UNESCO. https://www.unesco.org/en/articles/recommendation-ethics-artificial-intelligence

UNESCO. (2025). The Ethics of Artificial Intelligence.

Van Dijck, J. (2014). Datafication, dataism and dataveillance: Big Data between scientific paradigm and ideology. Surveillance & Society, 12(2), 197–208. https://doi.org/10.24908/ss.v12i2.4776

Wachter, S., Mittelstadt, B., & Floridi, L. (2017). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2), 76–99. https://doi.org/10.1093/idpl/ipx005

Zuboff, S. (2020). The age of surveillance capitalism: The fight for a human future at the new frontier of power (First trade paperback edition). PublicAffairs.

Downloads

Published

2025-12-31
Received 2025-12-19
Accepted 2025-12-31
Published 2025-12-31
Loading...