This talk moves from lived experience to system-level insight, using personal field research and applied design work to explore how meaning is lost as data scales, and what designers can do to preserve it. The structure intentionally mirrors the journey of data itself: from human story to abstraction to decision-making.
1. Grounding in Lived Experience: The talk opens with field research experiences from work in food insecurity, where individual stories reveal the complexity behind commonly reported metrics. This establishes the central tension between how people experience systems and how those experiences are represented in data.
2. The Gap Between Experience and Representation: Building on this foundation, the talk introduces the concept of a “meaning gap,” the distance between lived reality and the structured forms required by data systems. It explores how even accurate data can fail to convey the full context needed for responsible interpretation and decision-making.
3. Case Example: Absence, Trust, and Participation: The session expands this concept through work in clinical trial diversity, where underrepresentation in datasets reflects not just access issues, but deeper challenges of trust and engagement. This reframes “data gaps” as indicators of relational and systemic issues, rather than purely technical problems.
4. What Happens as Data Scales: The talk then examines how data transforms as it moves from local contexts into larger systems:
- Standardization and aggregation
- Loss of nuance and context
- Increasing abstraction in dashboards and reporting
- Integration into AI and automated decision-making systems
This section highlights how each step improves usability at scale, while also increasing distance from the original human experience.
5. Implications for AI and Decision-Making: As data becomes input for AI systems, the loss of context has amplified consequences. This section explores how:
- AI systems optimize for patterns rather than meaning
- Underrepresented populations remain invisible or misrepresented
- Outputs may appear authoritative while lacking contextual grounding
- Trust is affected when people do not see themselves reflected in systems
6. Reframing the Role of Design: The talk then shifts to the role of designers in this landscape, moving beyond interface design to responsibility for how meaning is carried through systems.
It introduces a reframing of design as:
- Preserving context across scale
- Making limitations and assumptions visible
- Supporting interpretation, not just presentation
- Designing for participation and trust
7. Practical Design Principles: The session concludes with a set of actionable principles that participants can apply in their work, including:
- Designing for what data cannot fully capture
- Treating absence and underrepresentation as meaningful signals
- Creating mechanisms to preserve and reintroduce context
- Designing AI outputs that support understanding and questioning
- Staying connected to lived experience through ongoing engagement
Participant Takeaways
1. A deeper understanding of how meaning is lost as data scales across systems and into AI
2. A new lens for interpreting data gaps as signals of trust, participation, and systemic issues
3. Practical principles for preserving human context in data-driven and AI-enabled systems
4. A reframed understanding of the designer’s role as a steward of meaning, not just a creator of outputs
5. Concrete ways to apply these ideas in complex, real-world design environments
