4.1.2 Ethical Use of AI and Emerging Technologies in Research

Navigating Opportunities and Challenges in Contemporary Scholarship

Introduction

Artificial Intelligence (AI) and emerging technologies are transforming research methodologies and scholarly communication. While offering unprecedented capabilities, their ethical use necessitates careful consideration. This article explores key ethical principles and best practices for integrating AI and novel technologies responsibly in doctoral research.

Opportunities Offered by AI and Emerging Technologies

  • Enhanced data analysis, pattern recognition, and automation of routine tasks (Jordan & Mitchell, 2015).
  • Improved access to information and collaboration through digital platforms.
  • Potential to accelerate discovery and interdisciplinary research.

Ethical Challenges and Considerations

Transparency and Accountability

  • Researchers must disclose the use of AI tools and algorithms in data processing and analysis (Floridi et al., 2018).
  • Understanding and communicating the limitations and biases of AI systems are essential.

Data Privacy and Security

  • AI applications often involve large datasets, raising concerns about participant confidentiality and data protection compliance (EU GDPR, 2016).
  • Secure data storage and responsible sharing practices are mandatory.

Bias and Fairness

  • AI systems can perpetuate or amplify biases present in training data, impacting research validity and fairness (Mehrabi et al., 2021).
  • Critical evaluation and mitigation strategies are necessary.

Intellectual Property and Authorship

  • Clarify authorship and credit when AI contributes to content generation or data interpretation (Johnson & Verdicchio, 2017).
  • Consider implications for plagiarism and originality.

Best Practices for Ethical AI Use

  • Incorporate ethics training specific to AI and data science.
  • Engage interdisciplinary collaboration including ethicists, legal experts, and technologists.
  • Maintain openness to scrutiny and peer review of AI-assisted research methods.

Conclusion

Integrating AI and emerging technologies presents both transformative potential and ethical responsibilities. Doctoral researchers must balance innovation with adherence to ethical standards to ensure trustworthy and equitable scholarship.

References

  • EU GDPR (General Data Protection Regulation). (2016). Regulation (EU) 2016/679 of the European Parliament and of the Council.
  • Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., … & 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
  • Johnson, D. G., & Verdicchio, M. (2017). AI, Agency and Responsibility: The VW Fraud Case and Beyond. Ethics and Information Technology, 19(2), 87–102. https://doi.org/10.1007/s10676-017-9427-8
  • 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
  • Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A Survey on Bias and Fairness in Machine Learning. ACM Computing Surveys, 54(6), 1–35. https://doi.org/10.1145/3457607
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