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Module 5. Ethics, Context, and the Human Side of Data

Course 2: What is Data? Understanding the Building Blocks of Knowledge
Estimated Time: 30–35 minutes

🧭 Module Objectives

  • Explain why data are never neutral and always contextual.
  • Recognize ethical issues involved in collecting, using, and sharing data.
  • Understand the role of consent, representation, and cultural sensitivity in humanities data work.
  • Articulate principles for responsible and human-centered data practices.

Why Ethics Belong at the Center

It’s tempting to think of "data ethics" as a technical checklist: protect privacy, cite sources, follow copyright law. But in the humanities, ethics are not just about rules: they're about relationships.

Every dataset represents human choices and often human lives. Who gets recorded? Who doesn’t? Who controls the story that emerges? Ethical data work means treating data as people-shaped: always entangled with identity, dignity, and power.

Context Is Everything

Data divorced from its context can mislead, harm, or erase. Context includes:

  • Origins – who created or recorded it, under what conditions.
  • Purpose – why it was created or preserved.
  • Audience – for whom it was intended.
  • Interpretation – how meanings have shifted over time.

📘 Example: A protest song by Jesse Welles gains its meaning not only from the lyrics but from when and why it was written, and how audiences have responded to it online. Stripping away that context would flatten its human story.

In humanities data, maintaining context is a form of respect.

Even publicly available information may carry ethical complexities. When dealing with data that involve real people—living or recently deceased—we must ask:

  • Was consent given for this use or sharing?
  • Could publication cause harm or unwanted exposure?
  • Who benefits from this data being shared or analyzed?

The Belmont Report (1979) identified three enduring principles for ethical research involving humans:

  • Respect for Persons (informed consent and autonomy)
  • Beneficence (do good, minimize harm)
  • Justice (equitable treatment and benefit sharing)

Digital humanists have extended these ideas to include community agency: the right of individuals or groups to tell their own stories and control their own data. For this reason, our project will NOT submit any Jesse Welles (or other creator's) material to online large language model (LLM) generative AI platform (and we'll discuss the details of this stance in a future course on the ethical problems with such LLM-powered AI tools).

Representation and Cultural Sensitivity

Data collection often mirrors the inequalities of society. Colonial archives, museum catalogues, and even song databases may privilege certain voices and silence others.

Responsible data work involves critical reflexivity:

  • Ask whose perspectives are missing.
  • Avoid labels or categories that perpetuate bias.
  • Include community collaborators wherever possible.

For example, Indigenous data sovereignty movements emphasize the right of Indigenous peoples to govern the collection, ownership, and application of their cultural data. Similar principles are emerging in African, Asian, and diaspora digital heritage contexts.

📍In the Wellespring Project, ethical design means crediting sources, linking data transparently, and treating artists' work as living, contextual materials rather than extractable "content."

The Humanities Contribution: Empathy as Method

Ethical data practice is not just about compliance; it's about empathy. Where the sciences rely on control and reproducibility, the humanities excel at careful attention to meaning and consequence.

Humanistic data ethics asks:

  • What human experience lies behind this dataset?
  • What responsibilities do I have toward the people or cultures represented?
  • How might my interpretation affect others' understanding?

This approach transforms data from a static record into a shared moral project: a way of knowing the world with care.

Principles for Responsible Data in the Humanities

Principle Description Example in Practice
Transparency Make methods and
assumptions visible.		 Include metadata and
methodology notes.
Accountability Take responsibility
for errors or omissions.		 Acknowledge limitations
in data models.
Reciprocity Give back to data
communities.		 Share findings or tools
with the public.
Sustainability Preserve data responsibly
and ethically.		 Use open formats and
proper attribution.
Empathy Treat data subjects as
more than "objects."		 Write narratives that
honor human stories.

These principles can guide both small classroom projects and large-scale digital archives alike.

Key Takeaways

  • All data are contextual, value-laden, and ethically charged.
  • Respect for consent, privacy, and representation is essential.
  • The humanities' interpretive traditions offer a model for ethical awareness.
  • Empathy and care are core components of responsible data work.

Knowledge Check & Reflection

Suggested Readings & Resources

A LOT has been written about data over the past few decades, exploring the term from a range of different perspectives. We cannot provide a comprehensive bibliography on the subject but here are some particularly relevant sources for further reading, including some that specifically address the issue of ethics in the use of humanities data:

Updated on Nov 6, 2025