Home Artificial Intelligence Automation within the newsroom Ideation Exploration Product definition Development Operation and sunsetting Links

Automation within the newsroom Ideation Exploration Product definition Development Operation and sunsetting Links

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Automation within the newsroom
Ideation
Exploration
Product definition
Development
Operation and sunsetting
Links

How we develop products for automated content within the AI & Automation Lab

Image by Max Brandl / AI & Automation Lab, Bayerischer Rundfunk
First Step Ideation. Copy the chart: https://miro.com/app/board/uXjVMYzLK9o=/

Our product ideas to this point have emerged from our work on the interface of editorial and technology. Along with the editorial teams, we have a look at the several areas within the journalistic workflow where AI and automation might be used to support.

  1. Data often provides the idea for automated content. Once you may have created an automation process, different content might be generated from one data source.
    In our automated stock market texts, we use data from the Munich Stock Exchange to generate a every day top gainers/losers report of the Dax corporations. We also use the identical workflow to generate an automatic weekly review specifically on Bavarian company data.
  2. As described in our Data Driven Publishing concept, we consider automation as a way of personalization. In our Remix Regional project, we create automated metadata to regionalize existing radio news and possibly offer it to users of our radio web and app services.
  3. We’re also working with our colleagues within the investigative teams to develop ideas for AI-assisted research with large amounts of information.
    Within the case of the Xinjiang Police Files, for instance, we were capable of provide an open source translation model due to our own AI infrastructure. We translated sensitive text files from Mandarin into English under great time pressure on several machines in parallel.
  4. One other use case is the AI-supported automation of labor routines.
    Especially for repetitive tasks, equivalent to analysing hundreds of user comments, an AI model is usually a very helpful assistance system for the editorial team.
Second step Exploration. Copy the chart: https://miro.com/app/board/uXjVMYzLK9o=/

Within the exploration phase we condense the thought and create an idea:

  • A crucial query initially is: ? For a lot of challenges in a newsroom there are already existing solutions.
  • As well as, we evaluate our resources and people of the teams involved: automation at all times has to do with working over existing workflows, for which we want who can design and evaluate a latest system along with their existing jobs.
  • The work with the editorial team already starts within the exploration phase: First, we take a to grasp exactly how a product can best be integrated into the every day routine. A have a look at the editorial workflow helps to implement the tool at the suitable time and in the popular way (directly within the CMS, via Microsoft Teams, via email).
  • Our previous automation formats are based on data that’s converted to text. Subsequently, we research the : Is the info published reliably? And the way quickly? Should the info be published only after agency reports, for instance, the query arises whether the event of automated content pays off.
Third step product definition. Copy the chart: https://miro.com/app/board/uXjVMYzLK9o=/

With the product definition, the goals and requirements of the product are defined intimately. This includes the functionalities of the product, the goal group, the intended effect and the success criteria.

  • On this phase, all teams involved define the concrete and which (Key Performance Indicators) might be used to measure it.
    The goals, for instance saved time, have to be defined as a KPI and made measurable.
  • One other vital step is to define the . Here we’re guided by the agile development (minimum viable product), by which central functions are defined and an prolonged range of functions is described on this basis. (see Development)
Fourth step development. Copy the chart: https://miro.com/app/board/uXjVMYzLK9o=/

We start the event of the system by creating an . This helps us to quickly test the of the product and provides all teams involved a primary impression of it.

  • For the automation of content, we currently implement systems by which we use different wordings depending on the info basis. This manner we will be certain that that data just isn’t hallucinated (sometimes the case with generative AI) and the editorial team has full control over the style and structure of the text from the start.
  • We also try to indicate first results quickly when simplifying work processes: In our project “What’s there, what’s missing”, by which we use an AI model to investigate user comments in response to inquiries to the editorial team, the MVP already showed at an early stage which sort of comments the AI will detect. Nonetheless, AI models need numerous training data, which is why we partly artificially enhance data for the MVP (data augmentation) so as to quickly give a primary impression. In the midst of development, the synthetic data might be replaced by real data.
  • Throughout the development of the product, we work agile: alternate with from the editorial team and an prolonged user test.
Fifth and sixth steps Operation & Sunsetting. copy the Chart: https://miro.com/app/board/uXjVMYzLK9o=/

Within the AI & Automation Lab, we use our own infrastructure for the products we develop.

: https://miro.com/app/board/uXjVPoxqSsU=/#tpicker-content
The chart is meant as a development guide for automation products within the newsroom and might be used and adapted for other projects. Click on the title to repeat the Miro-Board or use the share icon to download it as a pdf.

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