Grief in the age of AI: Griefbots and online death spaces
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
This paper explores how grief is intertwined within artificial intelligence (AI) and other digital areas. It examines concepts such as the griefbot, an AI used to provide communication between the deceased and the bereaved, digital online memorial spaces to commemorate those who have passed, and digital immortality. While griefbots provide comfort to those who have lost a loved one, questions surrounding ethics of use, such as obtaining the consent of the deceased, using the deceased’s data, and respecting their privacy, remain relevant. The digital afterlife industry, which includes online memorials, puts into question several societal challenges. These challenges can lead to debates over who “deserves” the most to have access data and digital spaces. Capitalism and digital immortality may reveal power dynamics with the deceased. For instance, business leaders and public figures may leave behind a digital legacy to continue to wield authority beyond the life of their physical bodies. As societies continue to merge aspects of human lives (and deaths) into the digital world, we must address issues of consent, privacy, and equitable access. Grieving and remembrance must not be lost in the digital age.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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