Exploring Approaches to Data Literacy Through a Critical Race Theory Perspective

Document Title

Exploring Approaches to Data Literacy Through a Critical Race Theory Perspective

Document Information

• Subject Area: Data Literacy, Critical Race Theory, Human-Computer Interaction (HCI)
• Keywords: Data Literacy, Critical Race Theory (CRT), Human-Computer Interaction (HCI), Workplace, Racial Issues, Community, Education and Training, Data Practices, Data Equity, Social Structures

Research Background and Issues

Identified Problems or Challenges

• Significant racial, gender, and class inequalities exist in the field of data science, particularly affecting underrepresented minority groups.
• Data literacy education often relies on traditional methods, neglecting the social and cultural contexts behind data and racial issues.
• Data usage in the workplace tends to lack critical examination, as workplaces often discourage questioning and critique of data.

Importance

• The exclusionary culture in data science and technology work marginalizes underrepresented groups, impacting individual career development and hindering fairness in data analysis and technological advancement.
• Examining data practices through the lens of Critical Race Theory (CRT) can help address social injustices, especially in scenarios where racial issues and data equity intersect.

Research Motivation and Related Work

• The authors introduce Critical Race Theory (CRT) into data literacy research to challenge embedded racial inequities in traditional data practices and propose new design and educational approaches.
• Existing research highlights the importance of CRT in understanding algorithmic fairness and racial categorization in social data.
• Project Background: The study builds on the DataWorks training program, which provides foundational data science skills to underrepresented groups, aiming to foster participation in real-world data work environments.

Solutions

Methods or Solutions

• The authors designed a series of data literacy workshops, encouraging data workers to select personally relevant topics (e.g., gun violence, mental health, community change) for data collection, processing, and representation.
• CRT serves as the analytical framework for examining the activities, assumptions, and practices within the workshops.

Innovations

• Explicit application of Critical Race Theory to the design and analysis of data literacy.
• Integration of mechanical and socio-technical perspectives on data literacy, proposing an educational approach to critical data literacy.
• Interdisciplinary perspective: Combining HCI, learning sciences, CRT, and social practices.

Implementation Steps and Key Techniques

1. Topic Selection and Definition: Data workers choose social issues they care about.
2. Data Search and Collection: Locate data related to the chosen topics, emphasizing awareness of data source quality and context.
3. Data Processing and Analysis: Data workers learn to use tools (e.g., Excel) to create charts and other data representations.
4. Data Sharing and Presentation: Data workers present their findings, explaining their chosen topics and data analysis results.

Research Outcomes

Specific Results

• Thematic Analysis: Identified three major themes:
  • Epistemic agency, critical data literacy, and conflicts within workplace environments.
  • Intersections of racial issues and normalized racism in data work.
  • Individual experiences that are simultaneously communal and intersectional.
• Insights into Workplace Environments: Power dynamics and the influence of race and gender significantly determine whether workers exhibit critical thinking.
• Workshop Effectiveness: High engagement with personal topics facilitated individual learning and skill development.

Comparison with Existing Solutions

• Traditional data science education often overlooks social contexts and personal experiences. This study contributes by introducing a more sensitive and equitable design approach through CRT.
• Compared to technology-focused professional data training, the workshops emphasize social issues and empower participants with autonomy in topic selection.

Experimental or Evaluation Results

• Workers demonstrated a lack of critical examination of data context and sources during tasks, reflecting workplace environments and insufficient support.
• Presentations showed improvements in key skills but revealed challenges in transferring learned skills to personal projects, highlighting the disconnect between workplace practices and critical education.

Limitations and Future Directions

• Limitations: Limited sample size (only 4 data workers), and the definition of data literacy may be constrained by Western mainstream perspectives.
• Future Directions:
  • Expand sample size and conduct more in-depth analyses of CRT and data work.
  • Introduce structural designs in workplaces to support critical thinking.
  • Explore ways to better integrate personal experiences with professional skills.

Summary and Design Recommendations

• Recommendation 1: Critically engage in workplace design to challenge traditional non-critical labor practices.
• Recommendation 2: Provide support to help beginners understand the social context and impact of data technologies.
• Recommendation 3: Explicitly incorporate racial issues into data literacy design and recognize the dangers of latent racism.
• Recommendation 4: Encourage combining personal interests with professional practices while promoting skill transfer and critical reflection.

Through in-depth analysis of data and social issues, this study demonstrates how adopting a critical perspective can promote data equity and social justice, underscoring the urgency of transforming the culture and core values of the computing field.