The future of artificial intelligence: Insights from recent Delphi studies
Bibliographic record
Abstract
We review thirteen Delphi studies on the future of Artificial Intelligence (AI), published between 2014 and 2024. Using the Delphi method, an iterative approach that refines expert insights through multiple rounds, these studies provide foresight into AI’s technological advancements, societal impacts, and policy implications across various sectors. For example, Delphi studies in healthcare foresee significant advancements in AI-driven diagnostics and personalized medicine, while in manufacturing, AI is anticipated to enhance human-robot collaboration and supply chain optimization. AI’s impact on journalism and photography shows promise in automating processes and enriching immersive storytelling, although issues like data privacy and algorithmic bias are raised. This review emphasizes a primary focus on technology trajectories, examining anticipated developments and timelines, while also considering broader strategic foresight aspects. General challenges identified include equitable access, the need for robust data governance, and workforce upskilling to integrate AI responsibly. By synthesizing insights across these studies, we provide a structured overview of both opportunities and limitations in AI development, offering guidance for stakeholders to navigate AI's complexities and capitalize on its potential responsibly. In addition, we propose methodological recommendations, such as standardizing expert selection and diversifying perspectives to improve the quality of future Delphi studies. • Reviews recent Delphi studies on the future of AI. • Explores AI's impact in healthcare, manufacturing, photography and journalism. • Identifies key ethical, societal, and economic challenges in AI integration. • Recommends methodological improvements for future Delphi studies on AI. • Emphasizes the importance of AI regulation and interdisciplinary collaboration.
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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 | 0.164 | 0.206 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".