The rapid advancement of artificial intelligence (AI) technology is profoundly transforming the information ecosystem, reshaping the ways in which information is produced, distributed, and consumed. This study explores the impact of AI on the information environment, examining the challenges and opportunities for sustainable development in the age of AI. The research is motivated by the need to address the growing concerns about the reliability and sustainability of the information ecosystem in the face of AI-driven changes. Through a comprehensive analysis of the current AI landscape, including a review of existing literature and case studies, the study diagnoses the social implications of AI-driven changes in information ecosystems. The findings reveal a complex interplay between technological innovation and social responsibility, highlighting the need for collaborative governance strategies to navigate the tensions between the benefits and risks of AI. The study contributes to the growing discourse on AI governance by proposing a multi-stakeholder framework that emphasizes the importance of inclusive participation, transparency, and accountability in shaping the future of information. The research offers actionable insights for policymakers, industry leaders, and civil society organizations seeking to foster a trustworthy and inclusive information environment in the era of AI, while harnessing the potential of AI-driven innovations for sustainable development.

Akhtar, N., & Mian, A. (2018). Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey. IEEE Access, 6, 14410–14430. https://doi.org/10.1109/access.2018.2807385

Allied Market Research. (2023). Generative AI market size, share, competitive landscape and trend analysis report by component, by technology, by end user: Global opportunity analysis and industry forecast, 2023–2032. Available online: https://www.alliedmarketresearch.com/generative-ai-market-A47396 (accessed on 19 June 2024).

Barocas, S., & Selbst, A. D. (2016). Big Data’s Disparate Impact. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2477899

Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency.

Bishop, C. M. (2006). Pattern recognition and machine learning. Springer Google Schola, 2, 1122–1128.

Bommasani, R., Hudson, D. A., Adeli, E., et al. (2021). On the opportunities and risks of foundation models. arXiv, arXiv:2108.07258.

Bostrom, N., & Yudkowsky, E. (2018). The ethics of artificial intelligence. In: Artificial intelligence safety and security. Chapman and Hall/CRC.

Bolukbasi, T., Chang, K. W., Zou, J. Y., et al. (2016). Man is to computer programmer as woman is to homemaker? debiasing word embeddings. In: Advances in Neural Information Processing Systems. Mit Pr.

Brown, T., Mann, B., Ryder, N., et al. (2020). Language models are few-shot learners. In: Advances in Neural Information Processing Systems. Mit Pr. pp. 1877–1901.

Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. In: Conference on Fairness, Accountability and Transparency. PMLR.

Cath, C., Wachter, S., Mittelstadt, B., et al. (2018). Artificial intelligence and the ‘good society’: The US, EU, and UK approach. Science and Engineering Ethics, 24(2), 505–528.

Carlson, M. (2018). The robotic reporter: Automated journalism and the redefinition of labor, compositional forms, and journalistic authority. In: Journalism in an Era of Big Data. Routledge.

Chowdhery, A., Narang, S., Devlin, J., et al. (2022). Palm: Scaling language modeling with pathways. arXiv, arXiv:2204.02311.

Crawford, K. (2021). The atlas of AI: Power, politics, and the planetary costs of artificial intelligence. Yale University Press.

Crawford, K., & Paglen, T. (2021). Excavating AI: The politics of images in machine learning training sets. AI & SOCIETY, 36(4), 1399–1399. https://doi.org/10.1007/s00146-021-01301-1

Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.

Dosovitskiy, A., Beyer, L., Kolesnikov, A., et al. (2020). An image is worth 16 × 16 words: Transformers for image recognition at scale. arXiv, arXiv:2010.11929.

Diakopoulos, N. (2016). Accountability in algorithmic decision making. Communications of the ACM, 59(2), 56–62. https://doi.org/10.1145/2844110

Dziri, N., Kamalloo, E., Mathewson, K., et al. (2019). Evaluating Coherence in Dialogue Systems using Entailment. In: Proceedings of the 2019 Conference of the North.

Dziri, N., Madotto, A., Zaïane, O., et al. (2021). Neural Path Hunter: Reducing Hallucination in Dialogue Systems via Path Grounding. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing.

Elgammal, A. (2019). AI is blurring the definition of artist. American Scientist, 108(1), 18–21.

Falk, K. (2019). Practical recommender systems. Simon and Schuster.

Floridi, L., Cowls, J., Beltrametti, M., et al. (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

Floridi, L. (2019). Translating Principles into Practices of Digital Ethics: Five Risks of Being Unethical. Philosophy & Technology, 32(2), 185–193. https://doi.org/10.1007/s13347-019-00354-x

Gasser, U., & Almeida, V. A. F. (2017). A Layered Model for AI Governance. IEEE Internet Computing, 21(6), 58–62. https://doi.org/10.1109/mic.2017.4180835

Goldberg, Y. (2022). Neural network methods for natural language processing. Springer Nature.

Gulati, A., Qin, J., Chiu, C. C., et al. (2020). Conformer: Convolution-augmented transformer for speech recognition. arXiv, arXiv:2005.08100.

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

Haenlein, M., & Kaplan, A. (2019). A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence. California Management Review, 61(4), 5–14. https://doi.org/10.1177/0008125619864925

Haider, J., & Sundin, O. (2019). Invisible search and online search engines: The ubiquity of search in everyday life. Taylor & Francis.

Jackson, P. (1986). Introduction to Expert Systems. Addison-Wesley.

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

Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260. https://doi.org/10.1126/science.aaa8415

Just, N., & Latzer, M. (2017). Governance by algorithms: reality construction by algorithmic selection on the Internet. Media, Culture & Society, 39(2), 238–258. https://doi.org/10.1177/0163443716643157

Kusiak, A. (2018). Smart manufacturing. International Journal of Production Research, 56(1–2), 508–517. https://doi.org/10.1080/00207543.2017.1351644

Lee, J., Davari, H., Singh, J., et al. (2018). Industrial Artificial Intelligence for industry 4.0-based manufacturing systems. Manufacturing Letters, 18, 20–23. https://doi.org/10.1016/j.mfglet.2018.09.002

Martin, J. H. (2009). Speech and language processing: An introduction to natural language processing, computational linguistics, and speech recognition. Pearson/Prentice Hall.

Mulgan, T. (2016). Superintelligence: Paths, dangers, strategies. Oxford University Press.

Nardi, B. A., & O’Day, V. (2000). Information ecologies: Using technology with heart. Mit Press.

Nayak, P. (2019). Understanding searches better than ever before. Available online: https://blog.google/products/search/search-language-understanding-bert/ (accessed on 19 June 2024).

Nemitz, P. (2018). Constitutional democracy and technology in the age of artificial intelligence. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2133), 20180089. https://doi.org/10.1098/rsta.2018.0089

Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. In: Algorithms of oppression. New York University Press.

O’neil, C. (2017). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown.

Pariser, E. (2011a). The Filter Bubble: How the New Personalized Web Is Changing What We Read and How We Think. Penguin.

Pariser, E. (2011b). The Filter Bubble: What the Internet is Hiding from you. Penguin.

Ramesh, A., Dhariwal, P., Nichol, A., et al. (2022). Hierarchical text-conditional image generation with clip latents. arXiv, arXiv:2204.06125.

Ramesh, A., Pavlov, M., Goh, G., et al. (2021). Zero-shot text-to-image generation. arXiv, arXiv:2102.12092.

Russell, S. (2022). Artificial Intelligence and the Problem of Control. In: Perspectives on Digital Humanism. Springer.

Russell, S. J., & Norvig, P. (2016). Artificial intelligence: a modern approach. Pearson.

Schulman, J., Wolski, F., Dhariwal, P., et al. (2017). Proximal policy optimization algorithms. arXiv, arXiv:1707.06347.

Schwab, K. (2017). The Fourth Industrial Revolution. Crown Currency.

Sharda, R., Delen, D., & Turban, E. (2021). Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support. Pearson.

Szeliski, R. (2022). Computer Vision: Algorithms and Applications. Springer Nature.

Tatalovic, M. (2018). AI writing bots are about to revolutionise science journalism: We must shape how this is done. Journal of Science Communication, 17(01), E. https://doi.org/10.22323/2.17010501

Taylor, L. (2017). What is data justice? The case for connecting digital rights and freedoms globally. Big Data & Society, 4(2), 205395171773633. https://doi.org/10.1177/2053951717736335

van Dijk, J. (2020). The Digital Divide. John Wiley & Sons.

Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Science, 359(6380), 1146–1151. https://doi.org/10.1126/science.aap9559

Wallach, W., & Marchant, G. (2019). Toward the Agile and Comprehensive International Governance of AI and Robotics. Proceedings of the IEEE, 107(3), 505–508. https://doi.org/10.1109/jproc.2019.2899422

Wardle, C., & Derakhshan, H. (2017). Information disorder: Toward an interdisciplinary framework for research and policymaking. Council of Europe.

Weizenbaum, J. (1976). Computer power and human reason: From judgment to calculation. W H Freeman & Co.

Zuboff, S. (2023). The age of surveillance capitalism. In: Social Theory Re-Wired. Routledge.