DSSG 2022 Kicks off at Carnegie Mellon University
Data Science for Social Good
by rayid
1y ago
After a COVID break for a couple of years, the Data Science for Social Good Fellowship is back, in its new home, at Carnegie Melon University. We’re excited to kick off the program next week, welcome our 24 fellows, and work with them over the next 12 weeks on 6 projects in partnership with governments and non-profits locally, nationally, and internationally. The projects this year include: Working with Allegheny County Department of Human Services to help them reduce homelessness in the county through prioritized distribution of rental assistance resources Working with Vibrant Emotional Hea ..read more
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COVID-19 Response Policies: Uses of Data and Implications for Equity
Data Science for Social Good
by rayid
3y ago
COVID-19 Response Policies: Uses of Data and Implications for Equity Carly Jones, Nick Chan, Tobi Jegede, Emily Reece, Kit Rodolfa, Rayid Ghani (Carnegie Mellon University) INTRODUCTION Over the past year, the global COVID-19 pandemic has produced wide-ranging social, health, and economic harms demanding swift action from policymakers in response. The scope and speed at which policy decisions are being made in response is unprecedented, with new changes to regulations, shutdowns and reopenings, and allocations of relief resources to individuals and businesses announced daily across the country ..read more
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Top 10 ways your Machine Learning models may have leakage
Data Science for Social Good
by rayid
3y ago
Top 10 ways your Machine Learning models may have leakage Rayid Ghani, Joe Walsh, Joan Wang If you’ve ever worked on a real-world machine learning problem, you’ve probably introduced (and hopefully discovered and fixed) leakage into your system at some point. Leakage is when your model has access to data at training/building time that it wouldn’t have at test/deployment/prediction time. The result is an overoptimistic model that performs much worse when deployed. The most common forms of leakage happen because of temporal issues – including data from the future in your model because you have t ..read more
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Data Science for Social Good Adds UK Locations, Opens 2019 Applications
Data Science for Social Good
by dssg
3y ago
Data Science for Social Good Adds UK Locations, Opens 2019 Applications Now seeking students, staff and project partners for program’s seventh year The 2019 edition of the Data Science for Social Good (DSSG) Summer Fellowship will feature two new international sites in the UK, through collaborations with The Alan Turing Institute, the University of Warwick, and Imperial College London. The seventh year of DSSG, taking place in Summer 2019, is now accepting applications for prospective fellows, technical mentors, project managers, and project partners. Since 2013, over 200 graduate and undergra ..read more
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Improving Workplace Safety in Chile through Proactive Inspections
Data Science for Social Good
by dssg
3y ago
Improving Workplace Safety in Chile through Proactive Inspections Every year, thousands of Chileans are killed or injured in work-related accidents. This was recently brought to light during the 2010 Copiapó mining accident. Chile’s labor ministry, Dirección del Trabajo (DT), is tasked with increasing workplace safety through inspections and enforcement. But DT’s inspections are largely reactive: complaints come in and then an inspection is completed, often after an injury or death. Preventative inspections can help find safety issues before bad things happen. DT has started moving to pre ..read more
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Improving Traffic Safety in Jakarta Through Video Analysis
Data Science for Social Good
by dssg
3y ago
João Caldeira, Alex Fout, Aniket Kesari, Raesetje Sefala UPDATE: We are pleased to announce that this project team won a Highlighted Paper Award at the AI For Social Good NIPS2018 Workshop! Congratulations to the Jakarta Fellows! Improving Traffic Safety in Jakarta Through Video Analysis The World Health Organization (WHO) estimates that over 1.25 million people die each year in traffic accidents. Nearly 2000 such fatalities occur annually in Jakarta, Indonesia alone, making it one of the most dangerous cities in the world for traffic safety. These deaths are tragic, but many of them are preve ..read more
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Tackling Tenant Harassment in New York City: 
A Data-Driven Approach
Data Science for Social Good
by dssg
3y ago
Jerica Copeny, Samantha Fu, Rebecca Johnson, and Teng Ye Tackling Tenant Harassment in New York City: 
A Data-Driven Approach This summer, our team of Data Science for Social Good fellows at the University of Chicago has partnered with the New York City Mayor’s Public Engagement Unit (PEU) with the goal of helping them better target their outreach to tenants who may be experiencing housing-related issues (ranging from eviction to repairs to landlord harassment). New York City passed pioneering legislation guaranteeing low-income New Yorkers a right to free counsel in housing court, but these r ..read more
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Data Science for Social Good Announces 2018 Projects in Chicago and Lisbon
Data Science for Social Good
by dssg
3y ago
2018 Data Science for Social Good Goes Global, Tackling Diabetes, Tenant Harassment, Unemployment, and More Fellows in Chicago and Portugal adapt data science and AI approaches for projects with non-profits and international governments In Chicago and Portugal this summer, the science behind self-driving cars, virtual assistants, and targeted advertising will be repurposed for nobler pursuits: safer workplaces and streets, improved vaccination and diabetes treatment, reduced incarceration and school dropouts, and much more. The 2018 Data Science for Social Good (DSSG) summer fellowship is the ..read more
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Human Lessons Learned Implementing Early Intervention Systems in Charlotte and Nashville
Data Science for Social Good
by dssg
3y ago
Human Lessons Learned Implementing Early Intervention Systems in Charlotte and Nashville This is the third in our three-part series “Lessons Learned Deploying Early Intervention Systems.” The first part (you can read it here) discussed the importance of data science deployments, while the second blog post in the series discussed the technical challenges related to the implementation. This final part is about the other (and typically more important) considerations in the implementation and deployment of Data Science products: Humans. Background For the past two years, we have worked with multip ..read more
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Tech Lessons Learned Implementing Early Intervention Systems in Charlotte and Nashville
Data Science for Social Good
by dssg
3y ago
Tech Lessons Learned Implementing Early Intervention Systems in Charlotte and Nashville This is the second in our three-part series “Lessons Learned Deploying Early Intervention Systems.” The first part (you can find it here) discussed the importance of data science deployments. For the past two years, we have worked with multiple police departments to build and deploy the first data-driven Early Intervention System (EIS) for police officers. Our EIS identifies officers at high risk of having an adverse incident so the department can prevent those incidents with training, counseling, or other ..read more
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