I. Digital Journalism Start-Ups in India | University of Oxford Study
The following is a report conducted by Dr Rasmus Kleius and Arjit Sen talks about 6 indian journalism startups, each exploring a different method of a news format for the public. A large part of setting up a startup involves market research and understanding the public opinion about the consumption of news itself. With technology becoming an integral part of the indian society and with extremely low internet rates, it is no surprise that Indian consume a lot of content, especially News. Where individual news organizations have failed to make a mark, startups take up different methods to catch the attention of the public, eyeing at understanding what Indians wish to see. The report talks about different content based startups – The Quint and The Scroll, News aggregators with their secret sauce – InShorts and DailyHunt and those for non profit such as The Wire and Kabar Lahariya. The report also deeply talks about the business strategies and decisions which go into these startups, and how it affects the way their content.
Being a consumer of news, I think people should always keep in mind the working of any news organization or company which works in the field and add that as a factor before judging content being produced any organization.
II. Defending Against Neural Fake News | Allen Institute of Artificial Intelligence
The following is a research paper, having recently been published in the Conference for Neural Information Processing Systems. The paper talks about how computer science, specifically Natural Language processing has enabled high quality text generation systems and that use of such systems for spreading disinformation is inevitable. Interestingly the paper talks about how the link between systems generating such disinformation and systems which can detect them and that ironically both of them happen to be the same, ie Systems which happen to produce extremely human-like disinformation articles also happen to be the very systems which are capable of combating them. The paper does describe the the architecture of such a system in detail, however the article strongly looks at how various at the state of artificial intelligence systems and their methodologies in detecting disinformation.
I think the following article gives us a rather weird but extremely idea about how important it is to understand the science of fake news and how it works and how such studies although might seem difficult must be done and reported so that people develop better systems to combat it.
III. Cambridge Analytica – The power of Big Data and Pyschographics | Youtube
The most infamous organization known out there, in this video where CEO of Cambridge Analytica, Alexander Nix talks about how communication has moved from the traditional mass communication or blanket communication where a huge number of users are shown the same set of content at the same time to targeted communication, where each individual is shown specific content through a large number of services such a social media, set up box television and news polls. Alexander talks about how Big Data and Psychographic analysis which includes understanding human emotions, their state of mind and their response to events using a large number of data points helps in powering such technology, and tells his story through Ted Cruz’s election campaign. He talks about the complexity of news consumption by people and how science behind figuring out what people want to hear.
Although Cambridge Analytica is despised by society for its actions, the company however does talk about how communication and the way the media plays a huge role in today’s society of mass communication. Something we must be aware of.
III. Bias in Computer System
The following paper although published in 1996, talks about an important matter, which is biases present in computer systems. Although we assume systems to be non biased trivially, the way we sometimes architect systems and the design elements which we add, subconsciously induces biases which we don’t’ think twice about. Although when we tend to think of biases in systems, we usually think of societal based gender bias such as bias because of gender, color, etc, however a large number of biases exist which one should be aware of. This paper goes through an exhaustive list of those biases and tries to bring to the attention the viewers of these when building any intelligent system. The paper also talks about methods or pointers to keep in mind whenever we work towards building computer systems for digital purposes.
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