In a recent interview on the Sirasa Pathikada programme, Professor Rohan Samarajiva, Chair of LIRNEasia, shed light on the potential of Artificial Intelligence (AI) in addressing various challenges and emphasised the need for mindful use of this technology for public benefit. He began by explaining the basic concepts of AI, distinguishing between general AI and narrow AI using simple examples. Prof. Samarajiva then highlighted the application of AI in disaster management, specifically in predicting floods. Drawing attention to Google’s flood forecasting system for the Ganga River in India, he described how AI utilises data from rainfall forecasts, river levels, and historical flood data to generate flood risk maps. 
In a recent interview on the Sirasa Pathikada programme, Professor Rohan Samarajiva, Chair of LIRNEasia, shed light on the potential of Artificial Intelligence (AI) in addressing various challenges and emphasised the need for mindful use of this technology for public benefit. He began by explaining the basic concepts of AI, distinguishing between general AI and narrow AI using simple examples. Prof. Samarajiva then highlighted the application of AI in disaster management, specifically in predicting floods. Drawing attention to Google’s flood forecasting system for the Ganga River in India, he described how AI utilises data from rainfall forecasts, river levels, and historical flood data to generate flood risk maps. 
Just yesterday, I wrote about the new regulation being rolled out by Facebook. Here is a description by Farhad Manjoo of the nuts and bolts of its operation. The people who work on News Feed aren’t making decisions that turn on fuzzy human ideas like ethics, judgment, intuition or seniority. They are concerned only with quantifiable outcomes about people’s actions on the site. That data, at Facebook, is the only real truth.
In the context of LIRNEasia’s big data work, we intend to wrestle with these issues. If we are not getting our hands dirty with the data and the stories we extract from them, I fear the conversation will be sterile. First, students should learn that design choices in algorithms embody value judgments and therefore bias the way systems operate. They should also learn that these things are subtle: For example, designing an algorithm for targeted advertising that is gender-neutral is more complicated than simply ensuring that gender is ignored. They need to understand that classification rules obtained by machine learning are not immune from bias, especially when historical data incorporates bias.