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.


  • 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. 

  • Data science research in Sri Lanka: Human resource challenges and prospects

    Keynote presentation for South Eastern University, 10th Annual Science Research Sessions 2021, 30 November 2021 – by Rohan Samarajiva, LIRNEasia

  • Misinformation in Bangladesh: A Brief Primer

    Over the past decade, both internet penetration and digital media user base have increased substantially.

  • A Corpus and Machine Learning Models for Fake News Classification in Bengali

    We present a dataset consisting of 3468 documents in Bengali, drawn from Bangladeshi news websites and factchecking operations, annotated as CREDIBLE, FALSE, PARTIAL or UN-CERTAIN. The dataset has markers for the content of the document, the classification, the web domain from which each document was retrieved, and the date on which the document was published. We also present the results of misinformation classification models built for the Bengali language, as well as comparisons to prior work in English and Sinhala.

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