I. Artificial Intelligence: Practice and Implications for Journalism
This article talks about the recent advances in the field of Artificial intelligence and how it impacts the world of digital journalism. The paper also discusses automating menial tasks undertaken by journalists such as findings trends/patterns in the data, summarizing documents and records, and so on. The paper also talks about how AI can assist people working on investigative journalism. It also discusses future applications of AI, such as personalized recommendations for users and the positive and negative impacts of it. It also discusses how the public will react when they realize that an investigative or an informatory article has been authored by artificial intelligence. It also busts the myths about AI taking over the job of journalists and emphasizes how it can augment the efficiency of journalists.
The paper concludes by discussing why journalists should start acting like amateur data scientists. It emphasized the importance of having a human-in-the-loop instead of having total dependence on smart systems. It also discusses the two different types of people, where one tries to understand the working behind the world using data(breaking black boxes), whereas the other one tries to model the world using computers(building black boxes). It discusses why journalists need to break down black boxes instead of building black boxes, as the former provides insights about how different inputs affect the outputs.
II. How The Wall Street Journal is preparing its journalists to detect deep fakes
In the age of information, misinformation and disinformation are also widely present. This article discusses how journalists need to prepare for the age of deep fakes and how to detect them. The article discusses how deep fakes make reporting the news harder, given a video source since one needs to verify the source manually. The articles discuss the different types of deep fakes. The articles also talk about how deep fakes could be used for changing the outcomes of elections, targeting individuals and nations. The article also mentions how deep fakes can be used to deceive news organizations and therefore undermine the trustworthiness of the news agency. The article also discusses the negative impacts that occur when a news organization publishes an article relying on unverified imagery/videos. The article also talks about how fake news powered by deep fake spreads faster than a genuine article, thereby causing more havoc.
In the end, the article concludes that deep fake research will come up with effective detection techniques before the 2020 elections. Discussing this article in the class can enable us to learn a few simple techniques to spot deep fakes, thereby preventing us from spreading the wrong information.
III. Making Artificial Intelligence Work for Investigative Journalism
This article mainly discusses how artificial intelligence is used for investigative journalism(story writing) instead of article distribution or promotion. The author talks about “Computational journalism” which is about coming with articles given the dataset, which saves time because the investigators do not need to go through millions of documents to come up with an article of interest. The paper also discusses how the investigation of Panama Papers issue would have been much faster, if AI had been used. He also presents the challenges faced when using AI for investigative journalism, thereby explaining why AI hasn’t been successful for the investigative task. Instead the author discusses how AI can be used for the purposes of data-collection, data-wrangling, data-cleaning and linking different records. This article also highlights the limitations of AI.
The paper concludes by saying that AI cannot be used for core investigative purposes unless it evolves to a general artificial intelligence(like humans) which is capable of deciding something is right or wrong. Discussing this article in the classroom can enable us to learn how AI can be used to do menial tasks such as data-wrangling and this can teach us how to automate those menial tasks in our day-to-day lives.
IV. The Need to Reflect: Data Journalism as an Aspect of Disrupted Practice in Digital Journalism and in Journalism Education
This paper talks about the issues caused by the lack of open data and draws a fine line between big data analytics and data journalism since data journalists generally work with open data, which is not big(in terms of size). The paper discusses the critical issues with the datafication of the world, such as privacy and transparency. The author also discusses the importance of multiple data sources to understand complexly layered results. The author emphasizes the importance of teaching new skills to journalism schools on coding, mathematics, and reasoning so that they can make sense out of openly available data. The article also discusses why data journalism, although having the potential, was not a big game-changer in the field, commenting on the inflexibleness of journalists to learn new technologies in which they are not comfortable.
The article concludes by talking about the importance of using multi-disciplinary teams. The author ends by hoping to use data for encouraging transparency in journalism. Discussing this article in the classroom will enable us to learn about the reasons why data journalism was not a game changer and can give us insights about the skills required to make sense out of data, which is essential to everyone in this age of data.
