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Predicting Amendments via Right to Information Query Log Analysis
Nayantara Kotoky, Vijaya V Saradhi
Jun 16, 2017 (modified: Jun 19, 2017)ICML 2017 WHI Submissionreaders: everyone
Abstract:Amendments to laws are necessary to keep up with the changing needs of the society. Such a process is largely manual, and takes feedbacks from the society for the introduction of an amendment. The Right to information (RTI) Act 2005 gives Indian citizens the opportunity to interact with the government. The present paper discusses how analysis of the RTI system can assist in mining feedbacks that can suggest potential amendments, and the process and challenges associated with solving this problem. Extracting latent patterns from the RTI query-reply process via learning algorithms is the main task at hand; representation of the RTI data, identifying applicable learning models and most importantly interpreting the results in the context of amendments is at the heart of this research work.
TL;DR:Can learning algorithms predict amendments? If yes, how to approach the problem? What are the challenges?- a discussion.
Keywords:Right to Information, Query Log Analysis, Learning Algorithms, Data Representation
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