Search engine for discovering works of Art, research articles, and books related to Art and Culture
ShareThis
Javascript must be enabled to continue!

Machine learning methods for “wicked” problems: exploring the complex drivers of modern slavery

View through CrossRef
AbstractForty million people are estimated to be in some form of modern slavery across the globe. Understanding the factors that make any particular individual or geographical region vulnerable to such abuse is essential for the development of effective interventions and policy. Efforts to isolate and assess the importance of individual drivers statistically are impeded by two key challenges: data scarcity and high dimensionality, typical of many “wicked problems”. The hidden nature of modern slavery restricts available data points; and the large number of candidate variables that are potentially predictive of slavery inflate the feature space exponentially. The result is a “small n, large p” setting, where overfitting and significant inter-correlation of explanatory variables can render more traditional statistical approaches problematic. Recent advances in non-parametric computational methods, however, offer scope to overcome such challenges and better capture the complex nature of modern slavery. We present an approach that combines non-linear machine-learning models and strict cross-validation methods with novel variable importance techniques, emphasising the importance of stability of model explanations via a Rashomon-set analysis. This approach is used to model the prevalence of slavery in 48 countries, with results bringing to light the importance of new predictive factors—such as a country’s capacity to protect the physical security of women, which has been previously under-emphasised in quantitative models. Further analyses uncover that women are particularly vulnerable to exploitation in areas where there is poor access to resources. Our model was then leveraged to produce new out-of-sample estimates of slavery prevalence for countries where no survey data currently exists.
Title: Machine learning methods for “wicked” problems: exploring the complex drivers of modern slavery
Description:
AbstractForty million people are estimated to be in some form of modern slavery across the globe.
Understanding the factors that make any particular individual or geographical region vulnerable to such abuse is essential for the development of effective interventions and policy.
Efforts to isolate and assess the importance of individual drivers statistically are impeded by two key challenges: data scarcity and high dimensionality, typical of many “wicked problems”.
The hidden nature of modern slavery restricts available data points; and the large number of candidate variables that are potentially predictive of slavery inflate the feature space exponentially.
The result is a “small n, large p” setting, where overfitting and significant inter-correlation of explanatory variables can render more traditional statistical approaches problematic.
Recent advances in non-parametric computational methods, however, offer scope to overcome such challenges and better capture the complex nature of modern slavery.
We present an approach that combines non-linear machine-learning models and strict cross-validation methods with novel variable importance techniques, emphasising the importance of stability of model explanations via a Rashomon-set analysis.
This approach is used to model the prevalence of slavery in 48 countries, with results bringing to light the importance of new predictive factors—such as a country’s capacity to protect the physical security of women, which has been previously under-emphasised in quantitative models.
Further analyses uncover that women are particularly vulnerable to exploitation in areas where there is poor access to resources.
Our model was then leveraged to produce new out-of-sample estimates of slavery prevalence for countries where no survey data currently exists.

Related Results

Historicizing Modern Slavery: Free-Grown Sugar as an Ethics-Driven Market Category in Nineteenth-Century Britain
Historicizing Modern Slavery: Free-Grown Sugar as an Ethics-Driven Market Category in Nineteenth-Century Britain
AbstractThe modern slavery literature engages with history in an extremely limited fashion. Our paper demonstrates to the utility of historical research to modern slavery researche...
Swift's Explorations of Slavery in Houyhnhnmland and Ireland
Swift's Explorations of Slavery in Houyhnhnmland and Ireland
Swift recognized that “slavery” was an ambivalent term: on one hand, slavery can be seen as a biological imperative—a natural condition of the innately servile; on the other hand, ...
Intercultural Competence Development Among University Students From a Self-Regulated Learning Perspective
Intercultural Competence Development Among University Students From a Self-Regulated Learning Perspective
Abstract. Intercultural competence is defined as a lifelong learning task that can be developed in any intergroup situation. A self-regulated learning model is applied to better un...
Graphic Design for Children with Learning Disabilities Based on the Isaan Mural Painting
Graphic Design for Children with Learning Disabilities Based on the Isaan Mural Painting
The study of 'Graphic design for children with learning disabilities' is a study that delves into learning-disabled children in the Isaan region. The author used the survey to form...
Modern Slavery Disclosure Regulation and Global Supply Chains: Insights from Stakeholder Narratives on the UK Modern Slavery Act
Modern Slavery Disclosure Regulation and Global Supply Chains: Insights from Stakeholder Narratives on the UK Modern Slavery Act
AbstractThe purpose of this article is to problematise a particular social transparency and disclosure regulation in the UK, that transcend national boundaries in order to control ...
Unequal yoke: The paradox of religious slavery
Unequal yoke: The paradox of religious slavery
Slavery is a historical reality of most societies in Africa. Lately, there has been an outcry on the resurgence of slavery with modern trends that include religious slavery which h...
Neutrosophic Hybrid Machine Learning Algorithm for Diabetes Disease Prediction
Neutrosophic Hybrid Machine Learning Algorithm for Diabetes Disease Prediction
Because of its far-reaching effects, diabetes remains a major health problem on a worldwide scale. It's a metabolic illness that causes hyperglycemia and a host of other health iss...

Back to Top