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Data, Algorithms and Policy — LIRNEasia

On two occasions I have been asked [by members of Parliament], “Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?” I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question. 

— Charles Babbage, Passages from the Life of a Philosopher (1864), Chap. 5, 59.

We live in a society where machines, algorithms and humans intertwine; where the “consensual hallucination” of cyberspace is no longer a separate part of our lives, but a swamp through which we wade, leaving data trails for the world to see; where the wrong people put the wrong figures into the wrong machines and wonder why the answer isn’t right. Terms like “Big Data” and “AI” have become Rorschach blots on the public consciousness

LIRNEasia’s role is to participate in the public policy dialogue around our algorithmically-inclined society with critical research and technical expertise. Since 2013, as cross-disciplinary team of data scientists, lawyers, and social scientists, we have conducted our own analyses, engaged deeply with policy makers and with private, data-heavy organizations.

Documents

  • LIRNEasia presents their work on AI-driven poverty mapping at a premier AI conference

    Pinpointing where poverty is most severe and tracking its changes over time is crucial for helping communities effectively. However, traditional benchmarks like household surveys and national censuses often fall short—they’re expensive, slow, and infrequent. In countries like Sri Lanka, this means we’re often relying on outdated information, hindering our ability to respond to sudden economic shocks or disasters. On top of that, poverty cannot be determined by income data alone, rather its multidimensional, where factors such as infrastructure, access to services, and economic activity also play a role in determining a community’s well-being. To capture these complexities, our DAP team (Data, Algorithms, and Policy) explored something different: how to rethink the way we measure poverty in Sri Lanka using AI with non-traditional data sources? In this study, we used anonymized Call Detail Records (CDR) and remote sensing data—to estimate poverty levels at the most granular administrative levels (Grama Niladhari Divisions) in Sri Lanka. This work makes two main contributions. First, we adapted existing machine learning approaches to the Sri Lankan context, to provide fresh insights into the spatial distribution of poverty. Second, we address significant gaps in literature by proposing frameworks for validating model outputs on their ability to inform […]

  • Responsible AI in Asia: Draft Report Open for Comments

    LIRNEasia has drafted a regional (Asia) report for the Global Index on Responsible AI (GIRAI) that focuses on responsible Artificial Intelligence in the Asia region, which is open for public review until April 13, 2025. This report, the final output a Global Center on AI Governance (GCG)-funded project, exists in three main parts: The first section examines where Asia stands in the Global Index, identifying key trends and regional patterns. The second section contextualizes these findings through in-depth national case studies, highlighting both best practices and governance gaps. The final section takes a forward-looking approach, identifying the key developments that will shape AI governance in the region. This report was authored by Merl Chandana and Sukitha Bandaranayake, with the India case study written by Anushka Jain and Aarushi Gupta. Part II was co-authored by Merl Chandana, Sukitha Bandaranayake, and Ana Florido. We welcome feedback on any aspect of this draft report (i.e., content, organisation). Feedback can be provided in the comment box below, or emailed to sukitha@lirneasia.net.  

  • Responsible AI in Asia: Summary of Upcoming Report

    The following document is a summary of an upcoming regional report for the Global Index on Responsible AI (GIRAI) that focuses on responsible Artificial Intelligence in the Asia region.  The broader report, to be released in April 2025 as the final output of a Global Center on AI Governance (GCG)-funded project, was authored by Merl Chandana and Sukitha Bandaranayake from LIRNEasia, with the India case study written by Anushka Jain and Aarushi Gupta (of Digital Futures Lab, India). Part II was co-authored by Merl Chandana, Sukitha Bandaranayake, and Ana Florido. The report containing global findings of the Index can be found here.

  • Use of online job portal data in research and in practice: A review

    Last year we conducted research to explore the possibility of leveraging online job portal data for economic analysis in 13 Asia Pacific countries, as a part of a project for the Asian Development Bank. We examined the types of information available on major portals across the region, to discern the nature and format of available data. We also tested and refined methodologies to analyse a dataset comprising online job vacancies sourced from a Sri Lankan job portal, to demonstrate use cases for exploring  the impacts of shocks on the labour market. The first step in this exploration was to review where in practice online job portal data has been used, to identify the  methods and techniques available along with their strengths and limitations.  The full review is published below. It covers the following key areas: Existing uses and applications of online job portal (OJP) data been used for labor market analysis. Limitations and challenges of using OJPs and existing ways of addressing them. Other data sources that complement OJP data. Processing steps, methods, and techniques used in collecting and processing OJP data prior to analysis.

  • Data science for social good

    On 2nd October 2023, Research Manager and Team Lead (Data, Algorithms, and Policy) Merl Chandana, alongside Junior Researcher Chanuka Algama, held a session titled ‘Applied data science research for social good’ at the University of Kelaniya’s Department of Statistics and Computer Science. The session delved into LIRNEasia’s journey of forming a data science team and using large datasets to yield critical insights for public policy. They contrasted LIRNEasia’s applied data science approach with traditional academic research and private sector practices. Additionally, they highlighted the emerging ‘AI for Social Good’ movement and its potential as a career avenue. The slides used can be accessed below.

  • DRAFT: Using mobile call detail records (CDRs) and remote sensing data for spatial mapping of poverty

    By employing unsupervised and supervised machine learning techniques, we explore the feasibility of utilizing mobile call detail records (CDRs) as well as geographic information system (GIS) and remote sensing (RS) data to map poverty spatially

  • (Discussion Paper) Towards a Realistic AI Policy for Sri Lanka

    Many countries around the world have adopted artificial intelligence (AI) polices. However, Sri Lanka is yet to adopt one. This discussion paper considers factors that may be taken into account if an AI policy were to be drafted in Sri Lanka.

  • (Policy Brief) Sri Lanka’s Open Data Portal

    This policy brief looks at the current status of Sri Lanka’s Open Data Portal, and what may be done to improve it. 

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a regional ICT policy and regulation think tank active across the Asia Pacific