Understanding the Role of Big Data in Contemporary AI

Big Data Ethics through the Lens of Catholic Social Teaching: Upholding Stewardship

Introduction.pptx

AI systems rely on vast datasets to function, learn, and make decisions, and the ethical considerations surrounding data collection, ownership, and usage significantly shape their development. However, ethical considerations surrounding Big Data are complex due to novel dilemmas arising within the evolving cyberworld. In response, this study draws on the concept of stewardship within Catholic Social Teaching, with particular focus on “Laudato Si’,” and presents it as a guiding ethical principle for responsible data custodianship, upholding human dignity, ensuring accountability, and harnessing Big Data’s potential for the common good. Just as “Laudato Si'” highlights our interconnectedness within the biosphere, this parallels our interconnectedness within the datasphere—the digital ecosystem—underscoring the necessity of stewardship in both domains.

Stewardship, as a guiding ethical principle, shifts the focus from a purely utilitarian or rights-based approach to one that emphasizes responsibility, care, and respect for the common good. Central to this idea is trust, which is essential for the ethical handling of data in AI development. Without trust in how data is collected and used, individuals and organizations may be hesitant to share their data, limiting the resources available for training AI systems. A major ethical concern in AI is the risk of bias, especially when systems are trained on biased or unrepresentative datasets.

The study argues that certain forms of Big Data can be governed as shared resources or a ‘common heritage of humanity,’ supporting initiatives like data commons, open data movements, open government data, and data cooperatives, and emphasizing the potential for collaborative stewardship and equitable access for the public good. If Big Data is viewed as a shared global resource, there would be an increased emphasis on ensuring that AI systems are based on diverse and inclusive datasets, mitigating the potential for harm. The concept of Big Data as a common heritage suggests that the benefits and responsibilities of AI development should be shared worldwide, fostering international collaboration among technologists, ethicists, policymakers, and other stakeholders. This would encourage the design of AI systems not solely for profit but with a focus on their long-term societal impacts, such as global health, poverty reduction, climate change, etc.

Strong support for data commons, open data, and data cooperatives can make AI development more democratic and less dependent on big tech, shifting control toward individuals and communities (Harvard Business Review 2025). The Open Data Policy Lab (2025) further states that such models not only promote inclusivity and cultural diversity in AI but also create new roles in data stewardship, governance, and community-driven innovation.

This module is designed to be taught after students have developed a foundational understanding of AI technologies and Big Data applications. Building on this knowledge, students will critically explore the ethical implications and societal responsibilities associated with these technologies, applying specific framework like Catholic Social Teaching to propose ethical AI policies. It will help students analyze the role of Big Data in shaping the development and ethical deployment of AI technologies, integrating key ethical principles to ensure AI systems respect human dignity, promote the common good, and serve society responsibly.

Required Reading

“Big Data Ethics through the Lens of Catholic Social Teaching: Upholding Stewardship” by Ferdinand Tablan

Course Outcomes

By the end of this module, students will be able to:

Analyze the ethical debates surrounding Big Data and their implications for Artificial Intelligence from the perspective of Catholic Social Teaching.

Specific skills demonstrated:
- Students will demonstrate an understanding of the key ethical issues related to Big Data stewardship, such as fairness, data bias, accountability, data trust, and transparency.
- Students will analyze and apply ethical principles from Big Data stewardship and CST to the development and use of AI, considering the broader social, moral, and technological implications.
Assessment method:
- Multiple choice quiz where students examine AI-related ethical issues (e.g., algorithmic bias, data misuse, privacy concerns) through the lens of CST.
Assess the role of Big Data in shaping the development and ethical deployment of AI technologies.

Specific skills demonstrated:
- Students will understand the significance of Big Data in relation to Information Technology, specifically AI, and the ethical issues associated with data collection, privacy, consent, data misuse, and data bias.
- Students will critically examine the relationship between Big Data and AI outputs, particularly in terms of social justice, fairness, and accountability.
Assessment method:
- Group discussion or case study where students evaluate a real-world example of AI development and its reliance on Big Data (e.g., AI based hiring, predictive policing, or AI-based credit scoring). Assessment of student contributions based on their understanding of Big Data’s ethical implications and the social consequences of AI systems built on biased or incomplete data.
- Multiple Choice Quiz
Design an AI Ethics and/or Big Data policy or strategy that integrates ethical considerations from Catholic Social Teaching and relevant ethical traditions.

Specific skills demonstrated:
- Students will develop a detailed AI Ethics and/or Big Data policy or strategy that incorporates ethical principles from CST to ensure it aligns with ethical principles such as stewardship, human dignity, the common good, and social justice.[1]
- Students will demonstrate their ability to propose actionable guidelines for the ethical use of AI and Big Data in a community or organization.
Assessment method:
- Collaborative activity where students create a Big Data/AI ethics policy OR set of ethical guidelines for the development and use of AI in a specific domain (e.g., credit scoring, education, or law enforcement). Graded on creativity, feasibility, alignment with ethical principles, and the clarity of recommendations.
- A written reflection essay that tackles the role of Big Data and AI in promoting justice, equity, human rights, and the common good.
- Multiple Choice Quiz

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[1]It is possible to develop an AI ethics policy without explicitly addressing Big Data, but it may not be the most effective approach, especially if the AI systems the company uses or develops rely heavily on large datasets. AI and Big Data are often intertwined, as AI models typically require large amounts of data for training and improving performance. Ignoring Big Data considerations could result in gaps related to data privacy, security, ethics, and quality.

Data Management: AI systems depend on quality data for accurate outcomes, so how that data is managed (collection, storage, access) is vital.
Ethical Considerations: Both AI and Big Data raise similar ethical concerns around bias, fairness, privacy, and transparency.
Security Risks: Poor data security could impact the integrity and trustworthiness of AI systems.

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