Human at the Center: A Framework for Human‐Driven AI Development
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Artificial Intelligence (AI) systems increasingly shape many aspects of daily life, influencing our jobs, finances, healthcare, and online content. This expansion has led to the rise of human–AI systems, where humans communicate, collaborate, or otherwise interact with AI, such as using AI outputs to make decisions. While these systems have shown potential to enhance human capabilities and improve performance on benchmarks, evidence suggests that they often underperform compared to AI‐only or human‐only approaches in experiments and real‐world applications. Here, we argue that human–AI systems should be developed with a greater emphasis on human‐centered factors—such as usability, fairness, trust, and user autonomy—within the algorithmic design and evaluation process. We advocate for integrating human‐centered principles into AI development through human‐centered algorithmic design and contextual evaluation with real users. Drawing on interdisciplinary research and our tutorial at two major AI conferences, we highlight examples and strategies for AI researchers and practitioners to embed these principles effectively. This work offers a systematic synthesis that integrates technical, practical, and ethical insights into a unified framework. Additionally, we highlight critical ethical considerations, including fairness, labor, privacy, and human agency to ensure that systems meet performance goals while serving broader societal interests. Through this work, we aim to inspire the field to embrace a truly human‐centered approach to algorithmic design and deployment.
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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 | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it