Tools to foster responsibility in digital solutions that operate with or without artificial intelligence: A scoping review for health and innovation policymakers
Bibliographic record
Abstract
BACKGROUND: Digital health solutions that operate with or without artificial intelligence (D/AI) raise several responsibility challenges. Though many frameworks and tools have been developed, determining what principles should be translated into practice remains under debate. This scoping review aims to provide policymakers with a rigorous body of knowledge by asking: 1) what kinds of practice-oriented tools are available?; 2) on what principles do they predominantly rely?; and 3) what are their limitations? METHODS: We searched six academic and three grey literature databases for practice-oriented tools, defined as frameworks and/or sets of principles with clear operational explanations, published in English or French from 2015 to 2021. Characteristics of the tools were qualitatively coded and variations across the dataset identified through descriptive statistics and a network analysis. FINDINGS: A total of 56 tools met our inclusion criteria: 19 health-specific tools (33.9%) and 37 generic tools (66.1%). They adopt a normative (57.1%), reflective (35.7%), operational (3.6%), or mixed approach (3.6%) to guide developers (14.3%), managers (16.1%), end users (10.7%), policymakers (5.4%) or multiple groups (53.6%). The frequency of 40 principles varies greatly across tools (from 0% for 'environmental sustainability' to 83.8% for 'transparency'). While 50% or more of the generic tools promote up to 19 principles, 50% or more of the health-specific tools promote 10 principles, and 50% or more of all tools disregard 21 principles. In contrast to the scattered network of principles proposed by academia, the business sector emphasizes closely connected principles. Few tools rely on a formal methodology (17.9%). CONCLUSION: Despite a lack of consensus, there is a solid knowledge-basis for policymakers to anchor their role in such a dynamic field. Because several tools lack rigour and ignore key social, economic, and environmental issues, an integrated and methodologically sound approach to responsibility in D/AI solutions is warranted.
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How this classification was reachedexpand
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".