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

TL;DR

This paper introduces the Radical Questioning framework as a pre-project ethical tool to critically assess the necessity and potential harms of AI interventions in human trafficking, emphasizing ethical reflection over techno-solutionism.

Contribution

It presents a novel five-step ethical assessment framework, Radical Questioning, for evaluating AI projects involving marginalized groups and systemic injustice.

Findings

RQ reveals overlooked sociocultural complexities in HT interventions.

Guides away from surveillance-based solutions towards survivor empowerment.

Framework generalizes to other domains with contextual adaptation.

Abstract

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.