What Happens After Death? Using a Design Workbook to Understand User Expectations for Preparing their Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Digital data has become a key part of everyday life: people manage increasingly large and disparate collections of photos, documents, media, etc. But what happens after death? How can users select and prepare what data to leave behind before their eventual death? To explore how to support users, we first ran an ideation workshop to generate design ideas; then, we created a design workbook with 12 speculative concepts that explore diverging approaches and perspectives. We elicited reactions to the concepts from 20 participants (18-81, varied occupations). We found that participants anticipated different types of motivation at different life stages, wished for tools to feel personal and intimate, and preferred individual control on their post-death self-representation. They also found comprehensive data replicas creepy and saw smart assistants as potential aides for suggesting meaningful data. Based on the results, we discuss key directions for designing more personalized and respectful death-preparation tools.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it