Reframing AI-for-Good: Radical Questioning in AI for Human Trafficking Interventions
Bibliographic record
Abstract
This paper introduces Radical Questioning (RQ), a structured, pre-design ethics framework developed to assess whether artificial intelligence (AI) should be applied to complex social problems rather than merely how. While much of responsible AI development focuses on aligning systems with principles such as fairness, transparency, and accountability, it often begins after the decision to build has already been made, implicitly treating the deployment of AI as a given rather than a question in itself. In domains such as human trafficking, marked by contested definitions, systemic injustice, and deep stakeholder asymmetries, such assumptions can obscure foundational ethical concerns. RQ offers an upstream, deliberative process for surfacing these concerns before design begins. Drawing from critical theory, participatory ethics, and relational responsibility, RQ formalizes a five-step framework to interrogate problem framings, confront techno-solutionist tendencies, and reflect on the moral legitimacy of intervention. Developed through interdisciplinary collaboration and engagement with survivor-led organizations, RQ was piloted in the domain of human trafficking (HT) which is a particularly high-stakes and ethically entangled application area. Its use led to a fundamental design shift: away from automated detection tools and toward survivor-controlled, empowerment-based technologies. We argue that RQ's novelty lies in both its temporal position, i.e, prior to technical design, and its orientation toward domains where harm is structural and ethical clarity cannot be achieved through one-size-fits-all solutions. RQ thus addresses a critical gap between abstract principles of responsible AI and the lived ethical demands of real-world deployment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".