Deepfakes have become mainstream in the media, with AI-generated anchors presenting news on China’s Xinhua or on MBN in South Korea. The media needs to care and must grapple with bias and filter bubbles. Striking the “right balance” between recommendations by humans and personalization by Artificial Intelligence (AI) presents a significant challenge.
Strikingly, several research shows that, while people in most countries still favour the ideal of a neutral and objective journalism, a larger group of the youth population say they prefer only those news that shares their point of view – and they expect to engage with the news on multiple social platforms.
In more recent news, Microsoft has announced laying off a considerable number of journalists from their MSN in order to replace them with AI. These laying off people included contracted news producers, who have been curating news for MSN homepage. One of the terminated contractors said to the media, “It’s been semi-automated for a few months, but now it’s full speed ahead. It’s demoralising to think machines can replace us but there you go.”
Content and news organizations are making increasing use of AI systems to uncover data from multiple sources and automatically summarize them into articles or supporting research for those articles. Machine learning algorithms have been proven to be adept at finding patterns in textual data and uncovering the useful information that accurately summarizes the data inside. By using these advanced algorithms against enormous quantities of data from press releases, blog posts, comments, social media posts, images, video, and all sorts of unstructured content, journalistic organizations can get quickly up to speed on fast-breaking news developments and generate content that accurately summarizes changing situations.
BBC has been using news aggregation and content extraction APIs that helps the publication with tagging their news articles with relevant tags for journalists to find latest stories on particular topics quickly. Similar to this, to tag and annotate stories, The New York Times also uses tools which uses machine learning to tag articles for reporters’ usage. Advanced systems like these can aid reporters in expediting their research process and cover more grounds in a shorter span of time. These systems also help in fact-checking for journalists, which again is critical to report news authentically.
In the coming years automation will have a major impact on how journalists work. Learning basic AI/ML tools becomes an important consideration given redundant reporting processes are being automated. Not just as a preparation against future job automation, AI/ML holds tremendous value in things like fact-checking and deriving insights from various data resources.
When it comes to the use of AI in the newsroom, the debate tends to move away from journalism and toward algorithms, databases and machine learning. It is rare for mainstream news outlets to consider integrating AI technologies like robots into a functioning system. Even established media houses are still not there, and smaller news outlets are struggling to digest the fact that AI can help them in many ways.
The impact of AI on journalism is clearly seen with our advancements in the generation of algorithms that increasingly mimicked the way a human brain behaves and reacts. The changes were open with the automatic generation of news and with the use of bots that presented or retransmitted the news. Today’s AI capabilities do extend throughout the whole process of management of the news media including the ideation, creation, diffusion and in the consumption, both as entertainment and current affairs.
The future offers an almost unlimited range of possibilities, but not limited to news media or journalism but to social transformation: emotion recognition in relationship management, micro-segmentation of audiences to design/provide products in an individualized way, the possibility of creating products on demand in an immediate and personalized way, or instant availability of multilingual content, even with settings programmed to be broadcast with different types of voice or presented with different prototypes of people.
Note: As with many predictions reports there is a significant element of speculation, particularly around specifics, and the report should be read bearing this in mind. Having said that, any mistakes – factual or otherwise – should be considered entirely the responsibility of the author(s).
This blog post was written using Artificial Intelligence.