Digital Afterlives: Imagining Effective Policies and Regulations for Digital Remains
Bibliographic record
Abstract
The digital platforms we engage with daily are brimming with the digital traces of the dead. These digital remains – or the photos, videos, and text messages left behind by deceased users (Lingel 2013), have given rise to the digital death industry – an umbrella term for online platforms that offer services such as online memorialisations, virtual funerals, and graves, andinteractions with avatars of the deceased through chatbots and virtual reality (Öhman and Floridi 2017). Mourners turn to these digital infrastructures to express their grief and experience the continued presence of the deceased through their digital remains (Kasket 2020). Yet the presence of digital remains and the digital death industry presents several policy concerns related toprivacy, digital dignity, the question of access and ownership, and the potential for commodifying deceased user data (Kasket, 2019). Without effective policies and regulations that provide guidelines for service providers and internet users, the dead are not afforded privacy rights nor considerations for their digital dignity and protections for their postmortem data. Left exposed and with no legal safeguards in place, digital remains are vulnerable to offences with tremendous emotional implications for the bereaved.Guided by information ethics as a theoretical framework exploring questions related to creating, accessing, and collecting digital data (Bruneault et al., 2023), my lightning talk asks us to imagine constructing potential policies and regulations that serve to protect our posthumous digital footprint.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.103 |
| Scholarly communication | 0.028 | 0.049 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.017 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".