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4 Understanding Data Ethics
Note: Content in this section is adapted from: Atenas, J., Havemann, L., & Timmermann, C. (2023). Reframing data ethics in research methods education: A pathway to critical data literacy. International Journal of Educational Technology in Higher Education, 20(1), 11. https://doi.org/10.1186/s41239-023-00380-y
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By Javiera Atenas
Datafication—transforming all things under the sun into a data format and thus quantifying them—is at the heart of the networked world.
José van Dijck, 2017 The Datafied Society
Collecting, publishing, and using data require two key elements: data ethics and data protection (video).
- Data ethics refers to the key principles governing right and wrong across the data cycle—from collection to use.
- Data protection refers to national and international regulations regarding personal privacy, access, and processing of data.
Not all data—public or private—is publishable, and not all uses are harmless. Regulations exist to balance potential benefits against harms, particularly for vulnerable communities. In human-generated data, we must consider how data can be responsibly published and used while protecting individuals.
We live in a datafied society, where almost everything is continuously transcribed into data, quantified, and analysed (Van Es & Schäfer, 2017). Decisions by corporations and governments are increasingly data- and algorithm-driven, impacting the economy, education (example), policy (example), and even personal choices.
Understanding the ethical conundrums of data informs future use, including creating new datasets to predict or influence behaviour (Hand, 2018).
The rise of technologies that process human data creates social asymmetries between those with expertise/tools and those whose data is used (Belbis & Fumega, 2019). AI and other large-scale data practices necessitate ethical limits.
Data ethics is the responsible and sustainable use of data (Data Ethics EU) under the principle of “do no harm” (Internet Society). It is a social contract between public and data users (Buenadicha et al., 2019) and ensures adherence to human rights and personal data protection (BMJV, 2020).
The EU Digital Education Action Plan 2021–2027 (EU DEAP) encourages learners to engage critically, safely, and ethically with data and technology. Topics include ethics, sustainability, data protection, privacy, children’s rights, and bias (gender, disability, ethnic, racial). Embedding these principles in research or project-based learning requires awareness of values and concepts.
| Concept | Definition | In Literature |
|---|---|---|
| Fairness | Treat like cases alike and make special arrangements to prevent undeserved disadvantage. | De Cremer & Van Dijk (2003); Jo & Gebru (2020); Stoyanovich, Howe & Jagadish (2018); Hoffmann et al. (2018); Richterich (2018); Ienca et al. (2018); Hand (2018); Bertino et al. (2019); Jobin et al. (2019); Johnson (2014) |
| Equality | Rules apply to all unless public interest exempts. Everyone should be treated equally. | Tusinski Berg (2018); Bogroff & Guegan (2019); Bezuidenhout et al. (2020); Kazim & Koshiyama (2019); Puaschunder (2019); Corple & Linabary (2020); Johnson (2014) |
| Do No Harm | Prevent direct or indirect harm from data, including when combining datasets. | Vinck et al. (2019); Raymond (2017); Kitto & Knight (2019); Loukides et al. (2018); Berman & Albright (2017); Taylor et al. (2016) |
| Respect Autonomy | Enable informed decisions about personal data use. | Al-Nuaimi (2020); Buckingham & Crick (2016); Powell (2018); Wheeler (2018); Sloane (2019); Kumar et al. (2020) |
| Sovereignty | Data subjects decide when, what, and with whom to share data; refusal should not limit participation. | Kukutai & Taylor (2016); Walter & Suina (2019); Kukutai et al. (2020); Snipp (2016); Lovett et al. (2019); Ai-min & Jia (2015); Hummel et al. (2018) |
| Reduce Bias | Recognise and mitigate structural and epistemic biases; avoid prejudiced portrayals. | Tam & Kim (2018); Richterich (2018); Henderson (2019); Herschel & Miori (2017); Ienca et al. (2018); Mittelstadt et al. (2016); Buenadicha et al. (2019) |
| Privacy | Individuals have a right to withhold data unless there is an overriding public reason. | Richards & King (2014); Yao-Huai (2005); Pollach (2005); Schwartz (2011); Herschel & Miori (2017); Zimmer (2010); Lundberg et al. (2019); Stahl & Wright (2018) |
Atenas, Timmermann, and Havemann (2020)
Students must learn how data are constructed and operationalised, including historical biases and cultural prejudices.
Current ethical concerns include:
- Predictive analytics – Using personal, professional, and social data to forecast behaviour (shopping, streaming, health outcomes, insurance).
- Government surveillance and influence – Detectors gather data; effectors use data to influence behaviour (Hood & Margetts, 2007).
- Socioeconomic profiling – Predicting student performance, insurance pricing, or criminality based on race, gender, neighbourhood.
- Bias and inequality – Misuse of data in hiring, healthcare, and public services can reinforce discrimination.
Goal: Design data-led research and learning activities that address inequalities, improve quality of life, and foster fair, harmless, unbiased, and equal data practices.
Use the Data Ethics Canvas by the Open Data Institute:
- Select a social issue.
- Discuss it in groups using the canvas.
- Write a blog post reflecting on ethical considerations in the research project.