Automated solutions and AI technologies in journalism have been evolving a lot over the last few years. Artificial Intelligence (AI) creates multiple appealing opportunities from taking over the media routine by generating data-based content for news feeds or automatic research to substituting a human reporter as such.
And even though the latter is still quite far away, you wouldn’t want to miss the developments. Speakers of #MediaRevolution`s third season went through main AI solutions available on the market, threats of machine learning for journalism and shared thought on the perspectives of AI in the sector.
The Fix gathered the main highlights of the session.
Clare Cook, Value my news. “Value My News: AI experiments in re-inventing the newswire”:
Value my news is a UK data-based project that brings in content from independent local publishers into one platform calledPing!
The platform scans articles to identify common threads and provide an aggregate picture of local news developments. This can both reveal bigger stories hiding within local ones, or create more valuable stories by connecting the thread between localized events.
Machine learning picks news stories, scans them, and implements various tags, so readers can identify the content faster and more effectively in the information flow.
The main aim of the initiative is to study the mechanic of local journalism and create holistic solutions that connect large national outlets to local content creators – challenging the centralized new wire model.
Using AI algorithms allows a media company to see the gaps in news coverage and predict whether stories would get successful feedback from the audience.
Value my news also looks at new ways to uncover potential bias in the system and make local stories more value-add for the audience.
Ben Martin, Co-Founder of LOYAL AI. “Research, write and refine your articles faster with Loyal AI“:
LOYAL AI algorithms make the process of writing articles faster, more accurate and more reliable for users by automating story research and embedding it into the writing process.
The automated technology helps to add and find trustworthy sources, check out content of other publishers, and leverage SEO opportunities inside the content creation platform.
The solution allows users to easily receive key information from different parts of the world in real time. If journalists don’t want to see some sources, they can blacklist them.
Journalists can use a research assistant to refresh search results and find updates on relevant content.
Charlie Beckett, LSE Journalism AI project. “Future of AI in Journalism”:
The gap in adoption of AI and related solutions has been reduced over the past year. But there is a lag in this trend, as solutions need to be tested, new talent has to graduate… Nonetheless, media are becoming more inclusive of different perspectives and embracing new solutions.
One of the main challenges for media is a lack of specialists and knowledge of AI/ automated solutions. This is being overcome and there is a huge appetite for online traings, including the ones run free of charge by the LSE project for media professionals.
Technology for technology’s sake is not enough, the main point is that it should be used and augment the capabilities of journalists/ newsrooms.
The diversity of AI technologies makes it harder to deal with some issues related to them. However, collaboration with other media outlets can be a solution for dealing with common challenges.
AI only can’t drastically transform journalism. It works more like a tool, providing more opportunities for developing niche journalism, newsletters, and substack platforms. It is, however, part of a larger trend of the industry’s transformation and the market forces pushing for consolidation.
Journalism really needs more experts: like lawyers, tech professionals, and others with all the different backgrounds to reflect on the diverse world we’re living in. Media are slowly becoming more open that’s a reason for optimism
Open discussion: Threats, opportunities and practical application of AI/ML in journalism
There are challenges in getting engineering staff and technical resources. In this case, some media use AI third-party tools to create automated content. However, many news organisations don’t have the opportunity to use even these few options and are slow to adopt new solutions.
It takes a lot of time and resources to develop quality technologies on your own. So it’s better to use already available technologies that are much cheaper and easy to implement. The industry is getting better at this.
Media require a lot of time to build quality technologies and implement it into the working process. It’s also about the lack of funds, which puts off the process of implementing AI-powered solutions.
Having the flexible management team helps to fill all the gaps because of the sharing skills among the staff.
Working with constant deadlines makes it tough for media organizations to focus on the bigger picture. Lack of bandwidth can be a roadblock for some organizations.
Journalism can’t reflect the diversity of AI due to the lack of staff members. However, there is a growing number of new professions and career trajectories. Media need to make the most of this trend.