Good Tech, Bad Tech: Policing Sex Trafficking with Big Data

Richard Kjellgren

doi:10.5204/ijcjsd.2139

Abstract

Technology is often highlighted in popular discourse as a causal factor in significantly increasing sex trafficking. However, there is a paucity of robust empirical evidence on sex trafficking and the extent to which technology facilitates it. This has not prevented the proliferation of beliefs that technology is essential for disrupting or even ending sex trafficking. Big data analytics and anti-trafficking software are used in this context to produce knowledge and intelligence on sex trafficking. This paper explores the challenges and limitations of understanding exploitation through algorithms and online data. It also highlights the key dimensions of exploitation ignored in big data-oriented research on sex trafficking. By doing so, the paper seeks to advance our theoretical understanding of the trafficking–‍technology nexus, and it is argued that sex trafficking must be reframed along a continuum of exploitation that is sensitive to the social context of exploitation within the sex market.

Highlights

With the rise of big data analytics, actors in the tech industry are increasingly involved in the social world and apply their knowledge in attempts to resolve fundamentally social problems; sex trafficking is not exempt from this
To further elaborate upon this point, this paper seeks to address the following two questions: (1) How do the epistemological assumptions underpinning big data analytics contribute to the production of knowledge on sex trafficking? (2) Why has technology been so successfully constructed as a key facilitator of sex trafficking and, paradoxically, as the panacea to human exploitation? These questions will be explored through a theoretical framework consisting of feminist perspectives on migration, sexual labour and exploitation (Agustín 2006, 2007; Andrijasevic 2010; Doezema 2010; O’Connell Davidson 2013, 2015; Sanders et al 2018a) and critical data studies
Throughout this paper, it has been argued that research underpinned by epistemological assumptions associated with big data contribute to the reification of binary and simplistic understandings of sex trafficking

Summary

Introduction

With the rise of big data analytics, actors in the tech industry are increasingly involved in the social world and apply their knowledge in attempts to resolve fundamentally social problems; sex trafficking is not exempt from this. This paper seeks to illuminate the complexities of applying big data analytics in the context of policing sex trafficking and the challenges of researching vulnerability and exploitation within the sex market through the analysis of online data. To further elaborate upon this point, this paper seeks to address the following two questions: (1) How do the epistemological assumptions underpinning big data analytics contribute to the production of knowledge on sex trafficking? Mensikova and Mattmann (2018: 6) used sentiment analysis and machine learning to identify possible instances of sex trafficking from Backpage adverts and argued that ‘possible ideas for improvement could include completely removing any bias from the data’ This neglects the fact that bias is potentially present in all datasets and that all forms of interpretation are susceptible to human bias (Goldberg 2015; Symons and Alvarado 2016). The following five issues will be discussed in more detail: (1) a limited understanding of exploitation within the sex market; (2) an uncritical and inadequate understanding of the data; (3) unsubstantiated and unconvincing operationalisations of key concepts and general issues of measurement validity; (4) an unnerving opaqueness and lack of contextual details surrounding data and methods; and (5) an uncritical appreciation of the identified patterns

A Limited Understanding of Exploitation Within the Sex Market

Findings

Discussion

Concluding Remarks