Diffraction-in-action: Designerly Explorations of Agential Realism Through Lived Data

Document Title

Difraction-in-action: Designerly Explorations of Agential Realism Through Lived Data

Document Information

• Subject Area: Human-Computer Interaction (HCI), Data Design, Sensory Augmentation Design
• Keywords: Biodata, Material, Bodies, Data, Collaboration, Empathy, Being-with, More-than-human, Design, Design Research

Research Background and Problem

• Problems or Challenges:

  1. Data is often regarded as an objective and abstract "commodity fiction," ignoring its context, origin, and temporal factors.
  2. How can designers "interact" with data and go beyond traditional frameworks of reflection and representation?
  3. Lack of specific cases illustrating how agential realism can be practically applied in design research.

• Research Significance:

  1. Exploring the intertwining of data and daily life to provide methods for a more human-centered data-driven society.
  2. Reevaluating the systemic production of data, its potential issues, and meanings to drive innovation in HCI design methodologies.

• Research Motivation and Related Work:

  1. Using agential realism methodology to shift the focus from data "reflecting the world" to "co-creating" new meanings of data.
  2. Previous studies primarily focused on individual behaviors in data usage (e.g., lived informatics) but lacked emphasis on designers' perspectives and practices.

Solution

• Main Methods/Solutions: The authors propose a design method for "lived data," promoting diffractive data analysis aimed at understanding the ambiguity, temporality, and social context of data.

• Innovations:

  1. Introduced a novel perspective of data as a design material, not only using data but also treating data design itself as a practical issue.
  2. Emphasized "living with data" to generate new social, ethical, and technological connections.
  3. Operationalized agential realism theory to advance design research centered on human body data.

• Implementation Steps and Techniques: The authors elaborate on this method through several case studies:

  1. Case Study 1: Breathing Shell—using a pressure-sensing inflatable cushion to perceive breathing changes.

     • Applied soma design methods, engaging in prolonged breathing exercises and sensor interactions to redefine "breathing" data.
     • Innovatively designed a deformable material sensing platform to perceive and transmit breathing patterns.

  2. Case Study 2: Pee-ometer—exploring urination behavior data.

     • Used autobiographical design methods to model urination urges, gradually uncovering their social contextual characteristics.

  3. Case Study 3: Ripple—using color-changing fabric to display emotional skin conductivity data.

     • Demonstrated how biosensing data is "generated" through the interplay of social, material, and cultural phenomena.

  4. Case Study 4: Affect Health—exploring user interaction with skin conductivity data through an application.

     • Tested animated color visuals to help users interpret data in various life contexts.

  5. Case Study 5: Comparing manual logs and automated mobile movement data to explore differences in mobility data.

     • Highlighted the social infrastructure and contextual factors underlying data practices.

Research Outcomes

• Specific Outcomes:

  1. Extracted design principles:

     • Data design should be an open-ended, undefined process.
     • Resist the impulse for efficiency by revealing the complexity of data contexts through slow, long-term processes.
     • Preserve data ambiguity to allow multiple interpretations and diverse insights.

  2. Validated the practical feasibility of diffractive data design and its impact on design decisions through case studies.

  3. Expanded the data framework from a static tool to a dynamic phenomenon, shifting from representing the world to co-creating the world.

• Advantages:

  • Challenged traditional perceptions of data as a mirror reflecting phenomena, proposing a new method centered on phenomena with data as a design tool.
  • Extended the application paradigm of data as a design material through detailed cases of human body data measurement and interpretation.

• Experimental or Evaluation Results:

  • The case studies demonstrated that this method significantly enhances designers' understanding of data complexity and its ambiguous applications.
  • User testing revealed that the diverse interpretations of ambiguous data could help users discover new behavioral patterns or self-reflection pathways.

• Limitations and Future Directions:

  1. The research remains primarily human-centered, focusing on bodies and designers, without delving into possibilities in automated systems or machine learning.
  2. Most cases are situated within European and North American contexts, lacking perspectives on cultural, identity, and social diversity.
  3. Future research could extend similar methods to machine learning or non-human data domains, exploring more complex real-world application scenarios.