Module 6: Managing Data Responsibly and Ethically

Learning Objectives

• Analyze the implications of unethical data practices in educational and organizational settings.
• Promote responsible data habits among students, faculty, staff, and administrators.
• Create and advocate for policies and practices that prioritize data privacy, security, and transparency.

Exploring Ethical Principles of Data Use

Principles for ethical data use are grounded in concepts such as personal autonomy of users, beneficence of data, and transparency and reliability. Institutions should not be concerned only with the legal standards to which they must comply - such as FERPA and HIPAA - but also be dedicated to upholding rights because it is the morally proper thing to do. With this is mind, let us briefly explore each of these ethical principles in some detail:

• Personal autonomy refers to the rights of individuals to determine when and how data about them is collected, used, and shared [1]. This principle relates to the concept of “informed consent” - informing the public about how data will be collected and used and allowing them the opportunity to “opt out.”
• Beneficence of data refers to the practice of ensuring that data is only used in ways that are intended to benefit society, especially those whose data is being used [2].
• Transparency and reliability refer to the capacity to ensure that the data processes and algorithms used by developers are understandable, explainable, and trustworth - i.e., that information about how a data tool works is made available in an accessible format (and using accessible language) to the public [3].

These principles should not be considered static rules but rather dynamic guidelines that will require recalculations with the emergence of evolving technologies, legal requirements, and societal expectations. Ethical data use requires careful reflection and discussions about the potential consequences of data-driven decisions, and our higher education institutions have a responsibility to incorporate these core values into their culture and daily practices. In so doing, they can ensure that data use supports, rather than undermines, their educational and social missions.

Teaching Why Ethical Data Use Matters

We know that ethical data use is important, but how do we teach this to others? While it is good to provide some theoretical background on what these principles are and how they are integral to many of our core ethical beliefs in society, the most effective discussions will likely center on the practical implications of these policies and case studies of what happens when they go awry. For instance, an instructor might provide an example of data collected about marginalized patient populations from a health center without patients permission and used by researchers to evaluate the demographics and health conditions of these patients. Using this data without patient permission poses tremendous risks to those patients - their health and well-being and even their safety [4].

Additionally, students should reflect on important questions related to the stewardship of data, such as “who benefits from this data and its use?” and “what risks are posed if this data gets into the wrong hands?” The motivations of the owner of the data can often be just as significant as what the data represents. By encouraging learners to analyze these questions from multiple perspectives, such as that of data subjects, data owners, policy makers, and even malicious actors who may exploit the data, we can develop better understandings of why ethical data use is so important.

How Can We Promote Trustworthy Data Practices?

To create a campus culture that promotes trustworthy data practices, it is necessary to first have institutional buy-in from multiple units and administrators. An institution can communicate the value of these practices by developing clear data frameworks for their own data - such as a document that clearly defines the roles of data managers, the responsibility for managing data, and expectations for everyone who interacts with the data. This will secure the data on campus while also showing learners the importance of a clear and strong framework.

Beyond an institutional policy, this ethical culture can be promoted through educational activities, especially through their integration into data literacy instruction. Ethics should not be viewed as an add-on to data literacy, but rather a crucial component within it [5]. While concepts like AI transparency and algorithmic bias may have recently been topics of discussion only in computer and information science classrooms, they are now relevant to all learners to the same extent that information literacy concepts like misinformation and information overload. Members of the campus community should be empowered, through this instruction, to speak up if they observe questionable data practices, enhancing trustworthy management.

These activities can be supported by ensuring ongoing evaluation and accountability mechanisms within the institution. Including a range of voices in evaluating the state of the data culture on campus - such as students and faculty from all disciplines - can support the strengthening of this culture while avoiding unintended reinforcement of systemic inequities within the culture. All should feel comfortable in taking a leadership role and providing their expertise in technology ethics, not just those from highly technical disciplines.

Case Example

Seton Hall University Libraries offers a data ethics guide that provides a compelling overview of responsible data practices for both students and faculty at the university. This guide emphasizes transparency in data collection and usage for research, building in user privacy support through clear access policies, and promoting digital and data literacy through ongoing educational efforts. This case highlights how libraries, often at the intersection of information access and user data, can lead the way in establishing ethical standards that influence broader campus data practices.

Reflection Activity

In a computer document or notebook, write a brief reflection addressing the following prompts:

Imagine you discover that a student information system at your institution is collecting more personal data than necessary, and access controls are weak…

• What are the potential ethical concerns or risks that you can identify in this situation?
• How would you address this issue with your team?
• What steps would you take to promote more responsible data practices with this system?

Summary

Key to ensuring ethical and responsible data management practices is fostering trust among users of data through safeguarding individual rights and ensuring the integrity of data-fueled operations on campus. As data becomes increasingly important in decision-making in higher education, the risks of misuse and abuse are growing tremendously. This module has explored the core ethical principles that should guide data use, emphasized the importance of cultivating ethical awareness among all stakeholders, and provided practical strategies for promoting trustworthy practices. It is important that ethics instruction not only reaches those who work with data regularly. As noted in past modules, we are all consumers of data today - whether it is through our work or through or consumption of media.

Additional Resources

• University of California: Data Access and Data Ethics (video)
• U.S. Federal Data Strategy Program: Data Ethics Framework
• Roeschley & Khader (2020): Defining Data Ethics in Library and Information Science

References

1. Grafanaki, S. (2016). Autonomy challenges in the age of big data. Fordham Intellectual Property, Media and Entertainment Law Journal, 27, 803.
2. Jimerson, J. B. (2016). How are we approaching data-informed practice? Development of the survey of data use and professional learning. Educational Assessment, Evaluation and Accountability, 28, 61-87.
3. Lund, B., Orhan, Z., Mannuru, N. R., Bevara, R. V. K., Porter, B., Vinaih, M. K., & Bhaskara, P. (2025). Standards, frameworks, and legislation for artificial intelligence (AI) transparency. AI and Ethics, early view.
4. Floridi, L., & Taddeo, M. (2016). What is data ethics? Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2083), 20160360.
5. Kim, J., Hong, L., & Evans, S. (2024). Toward measuring data literacy for higher education: Developing and validating a data literacy self-efficacy scale. Journal of the Association for Information Science and Technology, 75(8), 916-931.