The Ethics of Humanitarian Innovation: Mapping Values Statements and Engaging with Value-Sensitive Design
Bibliographic record
Abstract
The humanitarian sector continually faces organizational and operational challenges to respond to the needs of populations affected by war, disaster, displacement, and health emergencies. With the goal of improving the effectiveness and efficiency of response efforts, humanitarian innovation initiatives seek to develop, test, and scale a variety of novel and adapted practices, products, and systems. The innovation process raises important ethical considerations, such as appropriately engaging crisis-affected populations in defining problems and identifying potential solutions, mitigating risks, ensuring accountability, sharing benefits fairly, and managing expectations. This paper aims to contribute to knowledge and practice regarding humanitarian innovation ethics and presents two components related to a value-sensitive approach to humanitarian innovation. First is a mapping of how ethical concepts are mobilized in values statements that have been produced by a diverse set of organizations involved in humanitarian innovation. Analyzing these documents, we identified six primary values (do-no-harm, autonomy, justice, accountability, sustainability, and inclusivity) around which we grouped 12 secondary values and 10 associated concepts. Second are two proposed activities that teams engaged in humanitarian innovation can employ to foreground values as they develop and refine their project’s design, and to anticipate and plan for challenges in enacting these values across the phases of their project. A deliberate and tangible approach to engaging with values within humanitarian innovation design can help to ground humanitarian innovation in ethical commitments by increasing shared understanding amongst team members, promoting attentiveness to values across the stages of innovation, and fostering capacities to anticipate and respond to ethically challenging situations.
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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 | 0.076 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.009 | 0.048 |
| Scholarly communication | 0.020 | 0.021 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".