New Technology / Ai Development
Track AI development, model progress, product releases, infrastructure shifts and strategic technology signals across the artificial intelligence sector.
HAI Seminar: Predicting Child Labor in Ghana's Cocoa Industry
Topic
Child Labor in Ghana's Cocoa Industry
Key insights
- Antonio Skillicorn, a civil engineering PhD candidate, explores machine learning applications to predict child labor risks in Ghanas cocoa industry, aiming to improve predictive accuracy through diverse data sets
- Skillicorns research aligns with a broader goal of enhancing supply chain transparency and addressing labor risks, particularly in the construction sector, which is highly susceptible to forced and child labor
- The study emphasizes the significance of material and design impacts on well-being, advocating for improved living conditions and ethical practices in supply chains at both individual and community levels
- Collaboration with NGOs like Design for Freedom has been vital in identifying child labor risks in construction, highlighting the need for innovative strategies in material sourcing to mitigate these risks
- The seminar discusses the challenges faced by smallholder supply chains in global food production, stressing the importance of understanding these issues to develop effective interventions
- The research underscores the necessity of responsible analytics and AI in supply chain management, aiming to foster more ethical and sustainable practices in agriculture and construction
Perspectives
Analysis of child labor in Ghana's cocoa industry highlights the interplay of economic factors, supply chain dynamics, and the need for targeted interventions.
Proponents of Ethical Supply Chains
- Highlight the importance of machine learning in predicting child labor risks
- Emphasize the need for fair pricing initiatives to reduce child labor
- Argue for the integration of geospatial data to enhance predictions
- Propose targeted interventions based on accurate data analysis
- Stress the significance of understanding socio-economic factors influencing child labor
Critics of Current Approaches
- Question the effectiveness of relying solely on machine learning models
- Critique the assumption that fair pricing will mitigate child labor
- Warn about the potential unintended consequences of environmental policies
- Challenge the adequacy of survey data in capturing child labor dynamics
- Point out the limitations of using satellite data without precise geolocation
Neutral / Shared
- Acknowledge the complexity of smallholder supply chains
- Recognize the role of NGOs and government in addressing child labor
- Identify the need for comprehensive assessments of interventions
Metrics
other
a good fraction of the global CO2 emissions
deforestation's contribution to CO2 emissions
Understanding this fraction is crucial for addressing climate change.
that kind of deforestation has been responsible for the last 15, 20 years for a good fraction of the global CO2 emissions.
income
less than $1.50 USD
average daily income of cocoa farming households
Low income perpetuates child labor and poverty.
their annual average income is less than $1.50 a day for those households.
hazardous_conditions
5%
percentage of children working specifically in the cocoa sector under hazardous conditions
Hazardous conditions threaten children's health and education.
5% of these children are working specifically in the Cocoa Sector where hazardous conditions are prevalent.
number of households
15,000 households units
number of households covered by the GLS7 survey
A large sample size enhances the reliability of the survey results.
covers 15,000 households
households_surveyed
3,000 households units
number of households covered by the Norke survey
This sample size is crucial for understanding child labor dynamics in cocoa-growing areas.
The Norke, which is a different survey that we're drawing from, was done by University of Chicago. And it covers a smaller number of households, 3,000 households.
imputation iterations
20
number of times new labels were imputed for children
Multiple iterations help account for uncertainty in the estimates of child labor prevalence.
we do this 20 times in this case
F1 score decrease
10%
decrease in F1 score when using only satellite features
This decrease indicates the limitations of satellite data in accurately predicting child labor.
we find about a 10% decrease in F1 score
AUC decrease
16%
decrease in AUC when using only satellite features
A significant drop in AUC highlights the inadequacy of satellite data alone for this analysis.
16% decrease in AUC
Key entities
Timeline highlights
00:00–05:00
Antonio Skillicorn's research focuses on using machine learning to predict child labor risks in Ghana's cocoa industry, aiming to enhance supply chain transparency. The study highlights the importance of ethical practices in construction and agriculture to improve living conditions and mitigate labor risks.
- Antonio Skillicorn, a civil engineering PhD candidate, explores machine learning applications to predict child labor risks in Ghanas cocoa industry, aiming to improve predictive accuracy through diverse data sets
- Skillicorns research aligns with a broader goal of enhancing supply chain transparency and addressing labor risks, particularly in the construction sector, which is highly susceptible to forced and child labor
- The study emphasizes the significance of material and design impacts on well-being, advocating for improved living conditions and ethical practices in supply chains at both individual and community levels
- Collaboration with NGOs like Design for Freedom has been vital in identifying child labor risks in construction, highlighting the need for innovative strategies in material sourcing to mitigate these risks
- The seminar discusses the challenges faced by smallholder supply chains in global food production, stressing the importance of understanding these issues to develop effective interventions
- The research underscores the necessity of responsible analytics and AI in supply chain management, aiming to foster more ethical and sustainable practices in agriculture and construction
05:00–10:00
Smallholder farms often operate informally, leading to low productivity and limited access to resources, which perpetuates child labor. There are opportunities for research and intervention to improve social and environmental outcomes in smallholder farming.
- Smallholder farms often lack formal operations, resulting in low productivity and limited resource access, which hinders their ability to secure credit and improve practices
- Families frequently rely on household labor for farming due to financial constraints, leading to childrens involvement in farm work and perpetuating child labor
- Unsustainable practices like deforestation are common as households seek to expand their farms, threatening environmental health and long-term agricultural sustainability
- There are significant opportunities for research and intervention to improve social and environmental outcomes in smallholder farming, potentially creating solutions that boost productivity while promoting sustainability
- Previous initiatives have aimed to incentivize better farming practices among smallholders in environmentally challenged regions, but complexities arise as increased productivity can lead to more deforestation
- Conditional incentives that reward farmers for avoiding harmful practices are being explored, with technology and monitoring methods proposed to ensure compliance and encourage sustainable farming
10:00–15:00
The European Union is advocating for zero deforestation in supply chains, which is crucial for addressing child labor in Ghana and Ivory Coast's cocoa sectors. Cocoa farmers in Ghana receive only 6% of the industry's total value, highlighting the urgent need for interventions to improve their livelihoods and reduce child labor.
- The European Union is promoting zero deforestation in supply chains, which is vital for tackling child labor in Ghana and Ivory Coasts cocoa sectors. Targeted incentives for smallholder farmers are essential to address these intertwined issues
- Cocoa farmers in Ghana receive only 6% of the industrys total value, with many surviving on less than $1.50 daily. This economic inequality underscores the urgent need for interventions to enhance their livelihoods and reduce child labor
- Child labor in cocoa farming exposes children to dangerous conditions, including sharp tools and heavy loads. These hazards not only threaten their health but also limit their educational opportunities, perpetuating poverty
- Despite programs aimed at assisting high-risk households, effective resource allocation remains a challenge. Proper targeting of interventions is crucial to ensure that support reaches the most vulnerable families
- The International Cocoa Initiative is utilizing data-driven methods, including machine learning, to combat child labor. These strategies aim to improve understanding of contributing factors and enhance prediction accuracy in a context of limited data
- Child labor prevalence in Ghanas cocoa industry is likely underestimated, with nearly one in four children involved in such work. This alarming statistic calls for immediate action to strengthen monitoring and support systems for affected families
15:00–20:00
Child labor remains a significant issue in Ghana's cocoa sector, with nearly 25% of children involved in such work. Efforts to combat this include frameworks from the International Cocoa Initiative and the Ghanaian government's adoption of child labor monitoring systems.
- Child labor is a major concern in Ghanas cocoa sector, with nearly 25% of children engaged in such work, impacting their health and education
- The International Cocoa Initiative has implemented frameworks that include awareness campaigns and direct support like school supplies to help children leave dangerous labor situations
- Accurate measurement of child labor prevalence is hindered by expensive surveys and social biases, but direct interviews with children have proven effective in gathering reliable data
- The research utilizes survey data and satellite geospatial features to enhance predictions of child labor risk, offering timely insights into the factors at play in Ghana
- The Ghanaian government is adopting child labor monitoring systems similar to those of the International Cocoa Initiative, reflecting a commitment to coordinated action against child labor
- Existing data from government and academic surveys is vital for understanding child labor, but integrating new data sources like mobile phone data could improve predictive accuracy
20:00–25:00
The research integrates two survey datasets to enhance predictions of child labor in Ghana's cocoa sector, addressing biases from traditional parental reporting methods. By incorporating geospatial data and epidemiological methods, the study aims to provide more accurate estimates and actionable insights for policy interventions.
- The research combines two survey datasets to enhance child labor predictions in Ghanas cocoa sector, addressing biases from traditional methods that rely on parental reports
- The Norke survey, which interviews children directly, shows a higher prevalence of child labor than the GLS7 survey, emphasizing the need to include childrens perspectives for accurate data
- Geospatial data, including cocoa-driven deforestation and urbanization, is integrated into predictive models to better understand environmental factors affecting child labor risk
- An epidemiological method is used to correct underreporting in the GLS7 dataset by incorporating insights from the Norke survey, aiming to improve child labor prevalence estimates
- By identifying common variables across datasets, the research creates strata for comparing child labor prevalence in households with similar characteristics, revealing hidden patterns
- The findings could guide policy interventions to reduce child labor in the cocoa industry, offering actionable risk profiles for stakeholders to monitor and address the issue
25:00–30:00
The study employs logistic regression and machine learning models to estimate child labor prevalence in Ghana's cocoa sector, addressing underreporting issues. By integrating multiple data sources, it aims to enhance the accuracy of child labor predictions and improve monitoring efforts.
- The study estimates child labor prevalence by comparing data from the GLS7 and NORC surveys, aiming to address underreporting in the GLS7 dataset due to biased parental reports
- Researchers define strata using common variables from both datasets to identify groups of children with similar characteristics, improving the accuracy of child labor probability estimates
- Logistic regression is employed to infer the true probability of child labor while adjusting for underreporting, enhancing the reliability of data for machine learning models
- Machine learning models like XGBoost and Random Forest effectively handle non-linearities, achieving high performance metrics with an out-of-sample AUC of 0.95 and an F1 score of 0.84
- Incorporating satellite-based features into predictive models shows limited performance improvement, yet these features may still aid in more frequent risk assessments between larger surveys
- Survey features alone can accurately identify high-risk households for child labor, highlighting the importance of integrating diverse data sources for effective monitoring