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.
A research brief which explores the key data sources, algorithmic techniques and roadblocks in applying remote sensing techniques for development.
A white paper exploring how bias in algorithms and data affect development problems, especially when they interact with socio-legal systems
This tour d’horizon examines the possible of uses of data to help stop or slow the spread of COVID-19 directly. It gives weight to what can be done in the short term.
A research paper exploring an alternative approach to address the concern of privacy in sharing big data datasets by generating privacy-preserving artificial call detail records (CDRs) in accordance with the desired macro features of the dataset.
A whitepaper outlining the development of an alternative socioeconomic index for Sri Lanka, using principal component analysis (PCA) and publicly available census data
An extended research abstract which identifies several criteria that can be used to identify mobile network call detail records (CDRs) affected by load sharing and establishes why that is a prevalent issue, especially in urban areas.
A research brief exploring the possibility of using remote sensing and neural networks to estimate the paddy crop extent in Sri Lanka
LIRNEasia’s comments on the Framework for a Proposed Data Protection Legislation for Sri Lanka of June 2019