Compassionate Digital Innovation: A Pluralistic Perspective and Research Agenda
Bibliographic record
Abstract
ABSTRACT Digital innovation offers significant societal, economic and environmental benefits but is also a source of profound harms. Prior information systems (IS) research has often overlooked the ethical tensions involved, framing harms as ‘unintended consequences’ rather than symptoms of deeper systemic problems. In response, this paper presents a problematization review that critiques and revises three widely held assumptions about digital innovations: (1) that they generate net benefits that outweigh the associated harm; (2) that their ethicality can be calculated through utility and (3) that their harms can be mitigated through technological, corporate or regulatory intervention. We argue that compassion provides a pluralistic ethical foundation that integrates the strengths of consequentialism, deontology, and virtue ethics. This framework prioritises serving all stakeholders, especially the most vulnerable, while avoiding harm. It sets a research agenda focused on addressing structural dysfunctions, amplifying marginalised voices, and fostering sustainable systems. By reimagining digital innovation as a force for the common good, this paper contributes to a more just and equitable digital future for all.
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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 | 0.037 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.008 | 0.064 |
| Scholarly communication | 0.021 | 0.020 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".