Automating the Achievement of SDGs: Robotics Enabling & Inhibiting the Accomplishment of the SDGs
Bibliographic record
Abstract
The role of robotics is rapidly growing in importance in the particular non-industrial application domains, affecting society, economy and the environment. Robot systems are typically developed to address a specific technical, service-type or economical need, but often their broader impact is insufficiently investigated, if at all. For robots to play a beneficial role at society-level in the future, it is important to identify the mainstream directions in the field that enable the UN Sustainable Development Goals (SDGs), and encourage their development. Similarly, it is required to understand the negative impacts some applications can have on the achievement of the SDGs, and to ensure societies have the ability to prevent or mitigate them. Inspired by an exploration of the role of artificial intelligence in achieving the SDGs, this paper presents a preliminary version of a consensus-based expert elicitation process on the role of robots - as enabler or inhibitor - for a more sustainable future. For every SDG, the authors were able to identify potential positive and negative impacts of robotics. It remains difficult, though, to sketch a simple and comprehensive overview of the different ways in which robotic applications can unfold (direct or indirect) impact. Existing projects and studies are not intuitively comparable because they take many different directions and are not at the same level of abstraction, technological readiness, or implementation. Derived from the findings, recommendations on future policy developments are considered.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".