AI, Data Analytics and Ethic Articles
Knowledge Graph: AI, Data Analytics, and Ethics. Author: Milena Jael SILVA. Ecolonical LAB.

AI, Data Analytics, and Ethics: 2018-2024

This curated corpus compiles a diverse range of academic references addressing the intersection of artificial intelligence (AI), data analytics, and ethics between 2018 and 2024. The collection provides an in-depth examination of various key topics, including the ethical implications of AI, governance frameworks, AI’s role in education, healthcare, and its impact on society. Spanning multiple disciplines, these studies explore how emerging technologies are reshaping societal norms and posing new ethical challenges. The selected works also offer practical insights into both theoretical frameworks and case studies, making this compilation a vital resource for researchers, policymakers, and professionals in the field of AI and ethics.

Key Topics Explored:

  • Ethical Governance of AI: Exploration of frameworks for managing the ethical challenges associated with AI development and deployment.
  • AI in Education: Studies focusing on the role of AI in shaping educational practices and the ethical concerns surrounding its implementation in learning environments.
  • AI in Healthcare: Examination of AI’s potential in healthcare, including discussions on data privacy, algorithmic biases, and patient care.
  • AI and Social Impact: Analysis of AI’s broader societal impacts, with a focus on inequality, data-driven decision-making, and its influence on public trust.
  • Data Privacy and Security: Reviews addressing the ethical dimensions of big data analytics, particularly in relation to privacy and surveillance.
  • Bias and Fairness in AI: Critical investigations into the presence of algorithmic bias in AI systems and efforts to ensure fairness and inclusivity in AI outcomes.
  • AI in Digital Phenotyping: Ethical considerations of using social media and other digital data as training data for AI models in mental health and behavior analysis.

2024

  1. Bond, M.; Khosravi, H.; De Laat, M.; Bergdahl, N.; Negrea, V.; Oxley, E.; Pham, P.; Chong, S. W.; Siemens, G. (2024). A Meta-Systematic Review of Artificial Intelligence in Higher Education: A Call for Increased Ethics, Collaboration, and Rigour. International Journal of Educational Technology in Higher Education, 21(1), 4. https://doi.org/10.1186/s41239-023-00436-z
  2. Camilleri, M. A.; Zhong, L.; Rosenbaum, M. S.; Wirtz, J. (2024). Ethical Considerations of Service Organizations in the Information Age. Service Industries Journal, 44(9-10), 634-660. https://doi.org/10.1080/02642069.2024.2353613
  3. Ifenthaler, D.; Majumdar, R.; Gorissen, P.; Judge, M.; Mishra, S.; Raffaghelli, J.; Shimada, A. (2024). Artificial Intelligence in Education: Implications for Policymakers, Researchers, and Practitioners. Technology, Knowledge and Learning. https://doi.org/10.1007/s10758-024-09747-0
  4. Jaiswal, A.; Shah, A.; Harjadi, C.; Windgassen, E.; Washington, P. (2024). Ethics of the Use of Social Media as Training Data for AI Models Used for Digital Phenotyping. JMIR Formative Research, 8, e59794. https://doi.org/10.2196/59794
  5. Manfren, M.; Gonzalez-Carreon, K. M.; James, P. A. B. (2024). Interpretable Data-Driven Methods for Building Energy Modelling—A Review of Critical Connections and Gaps. Energies, 17(4), 881. https://doi.org/10.3390/en17040881
  6. Sharif, M.; Uckelmann, D. (2024). Multi-Modal LA in Personalized Education Using Deep Reinforcement Learning Based Approach. IEEE Access, 12, 54049-54065. https://doi.org/10.1109/ACCESS.2024.3388474
  7. Swist, T.; Shum, S. B.; Gulson, K. N. (2024). Co-producing AIED Ethics Under Lockdown: An Empirical Study of Deliberative Democracy in Action. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-023-00380-z

2023

  1. Giovanelli, A.; Rowe, J.; Taylor, M.; Berna, M.; Tebb, K. P.; Penilla, C.; Pugatch, M.; Lester, J.; Ozer, E. M. (2023). Supporting Adolescent Engagement with Artificial Intelligence-Driven Digital Health Behavior Change Interventions. Journal of Medical Internet Research, 25, e40306. https://doi.org/10.2196/40306
  2. Masters, K. (2023). Ethical Use of Artificial Intelligence in Health Professions Education: AMEE Guide No.158. Medical Teacher, 45(6), 574-584. https://doi.org/10.1080/0142159X.2023.2186203
  3. McCradden, M.; Hui, K.; Buchman, D. Z. (2023). Evidence, Ethics and the Promise of Artificial Intelligence in Psychiatry. Journal of Medical Ethics, 49(8), 573-579. https://doi.org/10.1136/jme-2022-108447
  4. Stoykova, S.; Shakev, N. (2023). Artificial Intelligence for Management Information Systems: Opportunities, Challenges, and Future Directions. Algorithms, 16(8), 357. https://doi.org/10.3390/a16080357
  5. Yelne, S.; Chaudhary, M.; Dod, K.; Sayyad, A.; Sharma, R. (2023). Harnessing the Power of AI: A Comprehensive Review of Its Impact and Challenges in Nursing Science and Healthcare. Cureus Journal of Medical Science, 15(11), e49252. https://doi.org/10.7759/cureus.49252

2022

  1. Chuang, C. W.; Chang, A.; Chen, M.; Selvamani, M. J. P.; Shia, B. C. (2022). A Worldwide Bibliometric Analysis of Publications on Artificial Intelligence and Ethics in the Past Seven Decades. Sustainability, 14(18), 11125. https://doi.org/10.3390/su141811125
  2. Kelley, S. (2022). Employee Perceptions of the Effective Adoption of AI Principles. Journal of Business Ethics, 178(4), 871-893. https://doi.org/10.1007/s10551-022-05051-y
  3. Ramon Saura, J.; Ribeiro-Soriano, D.; Palacios-Marques, D. (2022). Assessing Behavioral Data Science Privacy Issues in Government Artificial Intelligence Deployment. Government Information Quarterly, 39(4), 101679. https://doi.org/10.1016/j.giq.2022.101679
  4. Rubeis, G. (2022). iHealth: The Ethics of Artificial Intelligence and Big Data in Mental Healthcare. Internet Interventions, 28, 100518. https://doi.org/10.1016/j.invent.2022.100518

2021

  1. Akter, S.; Dwivedi, Y. K.; Biswas, K.; Michael, K.; Bandara, R. J.; Sajib, S. (2021). Addressing Algorithmic Bias in AI-Driven Customer Management. Journal of Global Information Management, 29(6), 1-25. https://doi.org/10.4018/JGIM.20211101.oa3
  2. Cao, L.; Yang, Q.; Yu, P. S. (2021). Data Science and AI in FinTech: An Overview. International Journal of Data Science and Analytics, 12(2), 81-99. https://doi.org/10.1007/s41060-021-00278-w
  3. Kazim, E.; Koshiyama, A. S. (2021). A High-Level Overview of AI Ethics. Patterns, 2(9), 100314. https://doi.org/10.1016/j.patter.2021.100314
  4. Mullins, M.; Holland, C. P.; Cunneen, M. (2021). Creating Ethics Guidelines for Artificial Intelligence and Big Data Analytics Customers: The Case of the Consumer European Insurance Market. Patterns, 2(10), 100362. https://doi.org/10.1016/j.patter.2021.100362
  5. Pickering, B. (2021). Trust, But Verify: Informed Consent, AI Technologies, and Public Health Emergencies. Future Internet, 13(5), 132. https://doi.org/10.3390/fi13050132
  6. Ryan, M.; Antoniou, J.; Brooks, L.; Jiya, T.; Macnish, K.; Stahl, B. (2021). Research and Practice of AI Ethics: A Case Study Approach Juxtaposing Academic Discourse with Organisational Reality. Science and Engineering Ethics, 27(2), 16. https://doi.org/10.1007/s11948-021-00293-x
  7. Sparrow, R.; Howard, M.; Degeling, C. (2021). Managing the Risks of Artificial Intelligence in Agriculture. NJAS-Impact in Agricultural and Life Sciences, 93(1), 172-196. https://doi.org/10.1080/27685241.2021.2008777

2020

  1. Shneiderman, B. (2020). Bridging the Gap Between Ethics and Practice: Guidelines for Reliable, Safe, and Trustworthy Human-Centered AI Systems. ACM Transactions on Interactive Intelligent Systems, 10(4), 26. https://doi.org/10.1145/3419764

2018

  1. Harambam, J.; Helberger, N.; van Hoboken, J. (2018). Democratizing Algorithmic News Recommenders: How to Materialize Voice in a Technologically Saturated Media Ecosystem. Philosophical Transactions of the Royal Society A: Mathematical, Physical, and Engineering Sciences, 376(2133), 20180088. https://doi.org/10.1098/rsta.2018.0088

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