# Ask before you Build: Rethinking AI-for-Good in Human Trafficking Interventions

arXiv: https://arxiv.org/abs/2506.22512 · HTML: https://mlsanity.com/paper/2506.22512/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 51 references that are in the map.
- First posted on arXiv 26 Jun 2025.

## Builds on (most cited first, 7 of 7)

- [The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice](https://mlsanity.com/paper/2310.00907/) (2023)
- [Understanding artificial intelligence ethics and safety](https://mlsanity.com/paper/1906.05684/) (2019)
- [Semi-Supervised Learning for Detecting Human Trafficking](https://mlsanity.com/paper/1705.10786/) (2017)
- [TraffickCam: Explainable Image Matching For Sex Trafficking Investigations](https://mlsanity.com/paper/1910.03455/) (2019)
- [Unraveling the Interconnected Axes of Heterogeneity in Machine Learning for Democratic and Inclusive Advancements](https://mlsanity.com/paper/2306.10043/) (2023)
- [Perspectives on How To Conduct Responsible Anti-Human Trafficking Research in Operations and Analytics](https://mlsanity.com/paper/2006.16445/) (2020)
- [Survivor-Centered Transformative Justice: An Approach to Designing Alongside Domestic Violence Stakeholders in US Muslim Communities](https://mlsanity.com/paper/2303.00172/) (2023)

## Abstract (arXiv)

AI for good initiatives often rely on the assumption that technical interventions can resolve complex social problems. In the context of human trafficking (HT), such techno-solutionism risks oversimplifying exploitation, reinforcing power imbalances and causing harm to the very communities AI claims to support. In this paper, we introduce the Radical Questioning (RQ) framework as a five step, pre-project ethical assessment tool to critically evaluate whether AI should be built at all, especially in domains involving marginalized populations and entrenched systemic injustice. RQ does not replace principles based ethics but precedes it, offering an upstream, deliberative space to confront assumptions, map power, and consider harms before design. Using a case study in AI for HT, we demonstrate how RQ reveals overlooked sociocultural complexities and guides us away from surveillance based interventions toward survivor empowerment tools. While developed in the context of HT, RQ's five step structure can generalize to other domains, though the specific questions must be contextual. This paper situates RQ within a broader AI ethics philosophy that challenges instrumentalist norms and centers relational, reflexive responsibility.