Real-Time Measures of Poverty and Vulnerability

Over the past several years, a series of influential studies have shown that digital trace data – typically from satellite and mobile phone networks – can be used to estimate the stationary welfare of small populations. But effective policymaking often requires an understanding of welfare dynamics, i.e., how such populations respond to changes in their environment, such as new programs and policy interventions. Here, we develop a new approach for measuring the dynamic welfare of individuals remotely, based on the analysis of their mobile phone records. We calibrate this approach with an original high-frequency panel survey of 1,200 Afghans, and an experimental protocol that randomized the timing and value of an unconditional cash transfer to each respondent. We show that mobile phone metadata, obtained with the respondent's consent from Afghanistan's largest mobile phone company, can be used to estimate the social and economic well-being of respondents, including the onset of positive and negative shocks. We discuss the potential for such methods to transform current practices of policy monitoring and impact evaluation.

Exploring data science for public good in South Africa: evaluating factors that lead to success

In the pursuit of public service, governments have to oversee many complex systems. In recent years, data-driven methodologies have been adopted as tools to oversee and enhance service delivery. In this talk I will discuss the ways that the government of South Africa, and its agencies, use data tools as well as the policies and investments that they have been put into place; some of which have created a more enabling ecosystem while others have created difficulties and challenges. I will discuss the current data landscape from the lens of Open Data policies, data readiness policies, and human capital development initiatives. This talk will be a summary of the work we have done in the past four years. It will be a discussion of our observations, our successes, aspirations and challenges we encountered as we continue towards a data-driven governance.

Early lessons in ML4D from the field

In this talk, I will share lessons from our efforts on the ground in creating AI-for-social-good solutions, spanning cellphone-image-based anthropometry for babies, AI-enabled active case-finding in tuberculosis, and early pest detection in cotton farming. The promise of AI as a powerful aid for achieving global-development goals is bolstered by five current forces: large frontline workforces enabling service delivery and data collection, growing smartphone penetration providing compute, connectivity, imaging, localization, and interfaces, large tech-enabled development programs having established data pipelines, infrastructure, and processes, rural populations increasingly adopting technology, and strong policy and institutional support for AI in development. We recommend that AI-for-social-good efforts utilize these forces by piggybacking on large tech-enabled development programs to achieve scaled impact. I will provide examples for such programs, point to opportunity areas, list criteria for AI-for-social-good innovators to assess likelihood of scaled impact, discuss risks and mitigation strategies, and suggest frontier areas for AI-for-social-good research.

Machine Learning for Development: Challenges, Opportunities, and a Roadmap

Researchers from across the social and computer sciences are increasingly using machine learning to study and address global development challenges, and an exciting new field of "Machine Learning for the Developing World", or "Machine Learning for Development" (ML4D) is beginning to emerge. In recent work (De Arteaga et al., ACM TMIS, 2018), we synthesize the prominent literature in the field and attempt to answer the key questions, "What is ML4D, and where is the field headed?". Based on the literature, we identify a set of best practices for ensuring that ML4D projects are relevant to the advancement of development objectives. Given the strong alignment between development needs and ML approaches, we lay out a roadmap detailing three technical stages where ML4D can play an essential role and meaningfully contribute to global development. Perhaps the most important aspect of ML4D is that development challenges are treated as research questions, not as roadblocks: we believe that the ML4D field can flourish in the coming years by using the unique challenges of the developing world as opportunities to inspire novel and impactful research across multiple machine learning disciplines. This talk is based on joint work with Maria de Arteaga, William Herlands, and Artur Dubrawski.

Using machine learning to locate hidden graves in Mexico

In Mexico, for more than a decade now, families of disappeared people and authorities have been discovering mass graves where hundreds of bodies and human remains lie. Still, there is a universe of clandestine graves that remain to be found in order to give certainty and truth to the hundreds of families looking for their loved ones.

Through a collaboration with Human Rights Data Analysis Group (HRGDAG) and the Human Rights Program at Universidad Iberoamericana we modeled the probability of identifying a hidden grave in each county in Mexico. In this project, we use a Random Forest to model for each county the probability of identifying a hidden grave using a set of independent variables and data about graves. With perfect precision (i.e., no false positives and no false negatives), the model predicts whether one or more clandestine graves will be discovered.

For the first stage of this project we used the dataset of graves identified that Universidad Iberoamericana built through analyzing newspapers. For the second stage, the results also included information from the local prosecutor’s offices at each county. This allowed us to compare and evaluate the accuracy of our model.

The next step is to map the clandestine graves of 2017 and 2018 in order to use predictions to identify hidden graves in 2019. Knowing where to search will help to create better public programs regarding missing persons in Mexico, helping to shape the search of missing people.

Taking "Big Data" evidence to policy: Experiences from the Global South

LIRNEasia has been working on leveraging big data for public purposes since 2012. As an organization situated in a developing country, we have experienced challenges in developing new insights, and informing policy and government processes. When leveraging big data and machine learning for development purposes, developing countries face three main inter-related challenges:

1. Skills: data scientists are in short supply and developing skills to make use of these new data sources become paramount. How should we build these skills? What should be the composition of research teams?

2. Data: accessing private sector data as well as government data can both be challenging. In an imperfect, often inconsistent regulatory environment, how can we facilitate responsible data access and use?

3. Policy impact and mainstreaming: Except in extreme cases most policy domains already have pre-existing established processes for generating and incorporating evidence in policy planning and implementation. How do we disrupt these ‘sticky’ processes with new forms of data and techniques?

This talk will address these three sets of challenges and our experiences in tackling them.