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4.3 Understanding Data Justice

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Content adapted from:

  • Atenas, J., Havemann, L., Rodés, V., & Podetti, M. (2023). Critical data literacy in praxis: An open education approach for academic development. Edutec. Revista Electrónica de Tecnología Educativa, (85), 49–67. https://doi.org/10.21556/edutec.2023.85.2851
  • Picasso, F., Atenas, J., Havemann, L., & Serbati, A. (2024). Advancing critical data and AI literacies through authentic and real-world assessment design using a data justice approach. Open Praxis, 16(3), 291–310. https://doi.org/10.55982/openpraxis.16.3.667
  • Atenas, J., Havemann, L., & Timmermann, C. (2020). Critical literacies for a datafied society: Academic development and curriculum design in higher education. Research in Learning Technology, 28. https://doi.org/10.25304/rlt.v28.2468

What is Data Justice?

Data justice is an emerging interdisciplinary framework that examines how data practices—collection, analysis, interpretation, and use—shape power, inequality, and social outcomes. It extends beyond technical concerns to address the ethical, political, and social implications of data systems.

Taylor (2017) defines data justice as the need to ensure that people are treated fairly in the way they are made visible, represented, and governed through data. This perspective connects digital rights (e.g., privacy, access, ownership) with broader questions of social and environmental justice.

Building on this, Dencik and Sanchez-Monedero (2022) describe data justice as a framework that interrogates:

  • Who controls data systems
  • Whose data is collected and why
  • Who benefits and who is harmed
  • How power operates through data infrastructures

What is Data Justice Concerned With?

  • Fairness in how people are represented through data
  • Power relations between those who collect data and those who are subject to it
  • The wider social, political, and economic consequences of data use

Core Dimensions of Data Justice

Across the literature, data justice is understood as a multi-dimensional framework that examines how data practices shape power, inequality, and participation in society. Rather than focusing only on technical issues, these dimensions highlight how data systems influence who is visible, who has agency, and how decisions are made.

The following table summarises key dimensions commonly identified in the literature:

Dimension Description Key Issues Examples
Visibility and Representation Data determines who is seen and how they are represented. Marginalised groups may be overrepresented in harmful ways or excluded entirely. Over-surveillance; underrepresentation; biased datasets Disproportionate policing of certain communities; lack of demographic diversity in datasets
Participation and Agency Data justice calls for meaningful involvement in decisions about data collection, use, and governance. Limited consent; lack of control; restricted ability to challenge decisions Individuals unable to opt out of data collection or contest algorithmic decisions
Access and Power Access to data, tools, and skills shapes who can benefit from data systems. Power is often concentrated in institutions. Data divides; unequal access to resources; institutional dominance Universities or corporations controlling large datasets while communities lack access
Fairness and Accountability Data-driven systems can produce biased or discriminatory outcomes. Accountability mechanisms are essential. Algorithmic bias; lack of transparency; weak oversight Biased AI decision-making in hiring or grading systems without clear explanation
Structural and Institutional Contexts Data systems are embedded within broader socio-economic and political systems, including histories of inequality. Colonial legacies; economic power imbalances; systemic inequality Data practices that reinforce global inequities or exploit vulnerable populations

Beyond a Technical Issue

Importantly, data justice is not merely a technical issue. It addresses long-standing societal concerns, including:

  • Inequality
  • Discrimination
  • Exclusion
  • Surveillance
  • Governance

Key Questions

Data justice encourages us to critically examine the role of data in society by asking:

Who benefits from data?
Who is harmed?
Who is visible—and who is invisible?

  • These questions help reveal how data practices can reinforce or challenge existing power structures and social inequalities.

The Datafication Process

Datafication is the process through which everyday activities are transformed into data that can be collected, stored, analysed, and used for decision-making.

The process can be represented as:

flowchart LR

A[Everyday Activities] --> B[Data Collection]
B --> C[Data Aggregation]
C --> D[Analysis & Algorithms]
D --> E[Profiling & Classification]
E --> F[Decision-Making]
F --> G[Social Outcomes]

%% Adding complexity
G --> H[Behavioural Feedback]
H --> A

C --> I[Data Sharing & Markets]
I --> D

D --> J[Bias & Assumptions]
J --> E

F --> K[Institutional Actions]
K --> G

%% Styling with pastel colours and black text
style A fill:#FDE2E4,color:#000000
style B fill:#E2ECE9,color:#000000
style C fill:#FFF1E6,color:#000000
style D fill:#E0FBFC,color:#000000
style E fill:#F1E3F3,color:#000000
style F fill:#EDE7B1,color:#000000
style G fill:#D8E2DC,color:#000000
style H fill:#FFE5D9,color:#000000
style I fill:#E4C1F9,color:#000000
style J fill:#FFD6A5,color:#000000
style K fill:#CDEAC0,color:#000000
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Examples of everyday activities that generate data include:

  • Shopping online
  • Using social media
  • Travelling with digital tickets or navigation apps
  • Accessing healthcare services
  • Studying and learning online

As data move through this process, decisions may be made about individuals and groups, affecting areas such as employment, education, healthcare, credit, and public services. Data justice examines whether these processes are fair, transparent, and accountable.

Data Justice in Education

Recent research has emphasised the growing importance of applying data justice in educational contexts, particularly as institutions increasingly rely on data-driven technologies such as learning analytics, digital platforms, and artificial intelligence.

Pangrazio et al. (2024) argue that while these systems offer opportunities for enhancing teaching and learning, they also introduce significant risks that are often overlooked. In particular, they highlight three critical concerns:

  • Ethical implications of student data use: Educational data is often collected without fully informed consent or transparency regarding how it will be used.
  • Power imbalances between learners and institutions: Students typically have limited control over how their data is collected, analysed, or shared.
  • Long-term consequences of datafication: The increasing reliance on data can shape learners’ identities, opportunities, and educational trajectories in ways that may reinforce inequality.

In response, Pangrazio et al. propose a research and pedagogical agenda that centres:

  • Critical engagement with data practices, encouraging learners to question how and why data is used
  • Student rights and agency, ensuring learners have a voice in decisions about their data
  • Pedagogies that address inequality and bias, recognising that data systems are not neutral

This perspective positions education as a key site for advancing data justice, both in terms of institutional practices and classroom learning.

Data Justice and Critical Data Literacy

Data justice is closely linked to the concept of critical data literacy, which extends beyond technical competencies to include the ability to interrogate and challenge data systems.

Atenas et al. (2023) conceptualise critical data literacy as encompassing:

  • Understanding how data is produced, processed, and used
  • Recognising issues of bias, power, and inequality
  • Acting ethically and responsibly when working with data

A key feature of this approach is its emphasis on praxis—the integration of reflection and action. Learners are not only encouraged to analyse data critically but also to engage with real-world data practices and consider their social implications.

Similarly, Picasso et al. (2024) argue that embedding data justice into teaching requires a shift in assessment and curriculum design. They highlight the importance of:

  • Authentic, real-world learning experiences that connect theory to practice
  • Assessment methods that promote ethical reasoning, rather than purely technical performance
  • Opportunities to critically examine the societal impact of data and AI systems

Together, these approaches highlight that developing data literacy is not simply about skills, but about fostering critical awareness, ethical responsibility, and social engagement.

Interpreting Taylor’s Three Pillars of Data Justice

Taylor’s (2017) framework provides a useful lens for understanding how data practices can create or reinforce injustice across different dimensions:

  1. Visibility
  2. Engagement with Technology
  3. Non-Discrimination
flowchart TD
    A[Data Justice]

    A --> B[Visibility]
    A --> C[Engagement with Technology]
    A --> D[Non-Discrimination]

    B --> B1["Who is represented in data?"]
    B --> B2["Who is invisible or overexposed?"]

    C --> C1["Can people access and use data?"]
    C --> C2["Do people have control over technologies?"]

    D --> D1["Are biases reproduced?"]
    D --> D2["Are groups unfairly treated?"]

    %% Pastel styling with black fonts
    style A fill:#FDE2E4,color:#000000
    style B fill:#E2ECE9,color:#000000
    style C fill:#FFF1E6,color:#000000
    style D fill:#E0FBFC,color:#000000

    style B1 fill:#F1E3F3,color:#000000
    style B2 fill:#FFE5D9,color:#000000

    style C1 fill:#EDE7B1,color:#000000
    style C2 fill:#D8E2DC,color:#000000

    style D1 fill:#FFD6A5,color:#000000
    style D2 fill:#CDEAC0,color:#000000
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Visibility

Visibility refers to how individuals and groups are represented within data systems.

  • Some communities are over-surveilled, experiencing disproportionate monitoring (e.g., through policing or welfare systems)
  • Others are underrepresented or invisible, meaning their needs and experiences are excluded from datasets and decision-making

Data justice seeks to ensure that visibility is balanced and does not expose certain groups to harm while excluding others from representation.

Engagement with Technology

This pillar focuses on the unequal ability of individuals and communities to engage with digital technologies.

Key challenges include:

  • Unequal access to devices and internet connectivity
  • Variations in digital skills and literacy
  • Structural barriers related to language, disability, income, or geography

From a data justice perspective, equitable engagement requires not just access, but meaningful participation in digital and data-driven environments.

Non-Discrimination

Non-discrimination addresses how data systems can reproduce or amplify existing inequalities.

Examples include:

  • Biased recruitment algorithms that disadvantage certain groups
  • Unequal access to financial services based on algorithmic profiling
  • Predictive policing systems that reinforce discriminatory practices
  • Facial recognition technologies with unequal accuracy across demographic groups

Key Insight

Taken together, these perspectives highlight that data justice in education is not only about protecting learners, but also about empowering them to critically understand and shape the data-driven systems that affect their lives. By integrating critical data literacy and data justice into teaching and learning, education can play a transformative role in promoting equity, agency, and ethical engagement in the digital age.

Data Justice as a Social Framework

Data justice is not solely concerned with technology. It is a broader social and political framework that examines how data practices affect people's lives and opportunities.

It connects closely to concepts such as:

  • Human rights
  • Citizenship
  • Democracy
  • Ethics of care

By linking data practices to these wider concerns, data justice encourages critical reflection on the role of data in society.

Key Questions

Data justice asks:

How does data affect human dignity?

How does it shape societal power structures?

These questions shift attention away from technology alone and towards the social consequences of data-driven decision-making.

Multidimensional understanding of justice

While Taylor's model is widely used, other approaches provide additional perspectives on justice in datafied societies. Political philosopher Nancy Fraser proposes a multidimensional understanding of justice that can be applied to data-related issues.

Her framework identifies three dimensions:

  1. Redistribution – the fair distribution of resources, opportunities, and benefits.
  2. Recognition – the fair representation and respect of different identities, cultures, and social groups.
  3. Representation – meaningful participation in decision-making processes and governance structures.
flowchart LR
A[Justice] --> B[Redistribution]
A --> C[Recognition]
A --> D[Representation]

B --> B1[Economic fairness]
C --> C1[Cultural respect]
D --> D1[Political voice]

%% Pastel styling with black fonts
style A fill:#FDE2E4,color:#000000
style B fill:#E2ECE9,color:#000000
style C fill:#FFF1E6,color:#000000
style D fill:#E0FBFC,color:#000000

style B1 fill:#F1E3F3,color:#000000
style C1 fill:#FFE5D9,color:#000000
style D1 fill:#EDE7B1,color:#000000
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Applied to data justice, Fraser's framework encourages us to consider:

  • Who benefits economically from data?
  • Whose experiences and identities are recognised or ignored?
  • Who has a voice in shaping data policies and governance?

The Need for a Data Justice Framework

A key challenge in contemporary data-driven societies is that many data-related harms are often invisible to those affected. Individuals may be unaware that decisions about them are being influenced by data collection, analysis, and algorithmic processing.

This lack of visibility makes it difficult to identify, understand, and challenge unfair practices.

Key Challenges

  • Lack of transparency in how data are collected and used
  • Data merging across institutions, including collaborations between the state and private sector
  • Automated decision-making systems that affect people's opportunities and outcomes
  • Surveillance practices that monitor behaviour, often without meaningful consent

These challenges raise important questions about accountability, fairness, and the distribution of power in data-driven systems.

Ethical Question

At the heart of data justice lies a fundamental ethical question:

How can data be used without harming individuals or communities?

Addressing this question requires more than technical solutions. It demands critical consideration of rights, power, inclusion, and social responsibility.

Key readings

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