Week 9 Chapter 10 Discussion Question
Srinijh Reddy Chendi
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Data/Research Ethics
Introduction:
In today's data-driven world, the intersection of ethics and technology in the field of data and
research ethics poses significant challenges. Data and research ethics encompass the
principles and guidelines governing the responsible conduct of research, data collection,
analysis, and dissemination. As technology continues to advance rapidly, it introduces new
possibilities and complexities, raising ethical concerns surrounding privacy, consent,
transparency, and the equitable use of data. This paper explores the field of data/research
ethics, identifies technology drivers leading to ethical concerns, discusses central ethical
issues contributing to conflicts with technology, provides examples of potential conflicts, and
examines how society approaches handling these conflicts.
Defining the Field:
Data/research ethics involves the ethical considerations and principles governing the
collection, management, analysis, and dissemination of data in research settings (Ferretti et
al., 2021). It encompasses ensuring the protection of human subjects' rights, maintaining
confidentiality and privacy, obtaining informed consent, and adhering to professional codes
of conduct and regulatory requirements. Researchers are ethically obligated to conduct their
work with integrity, transparency, and accountability, while also considering the potential
social, cultural, and ethical implications of their research activities.
Identifying Technology Drivers:
The proliferation of advanced technologies such as artificial intelligence (AI), machine
learning, big data analytics, and data mining serves as a primary driver leading to ethical
concerns in data and research ethics. These technologies enable researchers to collect,
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analyse, and interpret vast amounts of data with unprecedented speed and accuracy. However,
they also raise ethical questions regarding data privacy, security, bias, and the potential for
unintended consequences. For example, AI algorithms may inadvertently perpetuate biases
present in training data, leading to discriminatory outcomes in research findings or decision-
making processes.
Central Ethical Issues and Conflicts with Technology:
Several central ethical issues contribute to conflicts with technology in the field of
data/research ethics. These include:
i. Privacy and Data Protection: The collection and use of personal data in research
raise concerns about privacy infringement and data security breaches (Alnajrani et
al., 2020). Advances in technology have made it easier to collect and analyse
sensitive information, raising ethical questions about consent, anonymization, and
data ownership.
ii. Informed Consent and Autonomy: Obtaining informed consent from research
participants is a cornerstone of ethical research practice. However, the
proliferation of online data collection methods and passive data collection
techniques (e.g., tracking cookies) complicates the process of obtaining informed
consent, raising questions about autonomy and transparency in research.
iii. Data Bias and Fairness: AI and machine learning algorithms are susceptible to
bias, reflecting and amplifying existing societal biases present in training data.
This bias can result in unfair or discriminatory outcomes in research findings or
decision-making processes, perpetuating inequities and undermining the integrity
of research results.
Example of Potential Conflict:
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An example of the potential conflict between ethics and technology in data/research ethics is
the use of facial recognition technology in research studies. Facial recognition technology has
the potential to revolutionize research methodologies by enabling researchers to analyze
facial expressions, emotions, and behavior patterns. However, the widespread deployment of
facial recognition technology raises significant ethical concerns regarding privacy, consent,
and surveillance. For instance, researchers may inadvertently capture and analyze facial data
without obtaining informed consent from individuals, leading to potential privacy violations
and ethical breaches.
Society's Approach to Handling Conflicts:
Society approaches handling conflicts between ethics, technology, and data/research ethics
through various means, including laws, regulations, professional guidelines, and ethical
frameworks. For example, regulations such as the General Data Protection Regulation
(GDPR) in the European Union and the Health Insurance Portability and Accountability Act
(HIPAA) in the United States establish legal requirements for data protection, privacy, and
informed consent in research settings. Professional organizations and research institutions
also develop ethical guidelines and codes of conduct to govern the ethical conduct of research
involving human subjects and data. Additionally, interdisciplinary collaborations between
ethicists, technologists, policymakers, and stakeholders facilitate ongoing dialogue and
debate on emerging ethical issues in data/research ethics, fostering awareness, accountability,
and responsible innovation in research practices.
Conclusion:
In conclusion, the field of data/research ethics intersects with technology in complex ways,
giving rise to ethical challenges and conflicts that require careful consideration and ethical
stewardship. By identifying technology drivers, discussing central ethical issues, providing
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examples of potential conflicts, and examining societal approaches to handling conflicts, this
paper sheds light on the intricate interplay between ethics, technology, and data/research
ethics. Moving forward, interdisciplinary collaboration, ethical reflection, and regulatory
oversight are essential for navigating these ethical challenges and ensuring the responsible
conduct of research in the digital age.
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References
Alnajrani, H. M., Norman, A. A., & Ahmed, B. H. (2020). Privacy and data protection in
mobile cloud computing: A systematic mapping study. Plos one, 15(6), e0234312.
Ferretti, A., Ienca, M., Sheehan, M., Blasimme, A., Dove, E. S., Farsides, B., & Vayena, E.
(2021). Ethics review of big data research: What should stay and what should be
reformed?. BMC medical ethics, 22(1), 51.
