Latest / The In-Between Tech and Trust Podcast / Navigating the AI Slop: How Editorial Judgment Is Changing (EP 21)

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Navigating the AI Slop: How Editorial Judgment Is Changing (EP 21) · The In-Between Tech and Trust Podcast

• 0:14
  Hello, this is Eva and you're listening to the In Between Tech
• 0:18
  and Trust podcast. In this week's episode, we dive
• 0:21
  into one of the most relevant topics that you can look into in
• 0:25
  the AI era, and that is the intersection of AI media and
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  journalism. And for that topic, I have one
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  of the leading voices with me in conversation, and that is Doctor
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  Paul Elvas. And Paul is head of AI at funk
• 0:39
  comedian Cropper. He's also leading the AI lab
• 0:42
  within the company, so he brings his practical view into
• 0:47
  translating between stakeholders and implementing AI into the
• 0:52
  journalistic aspects of how to publish content and contain
• 0:56
  audience trust. So together we dive.
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  Into how AI intersects with trust, transparency, and the
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  journalistic. Practice, Paul also shares.
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  His view on how an AI newsroom of the future should look like.
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  We also look into the impacts of the so-called AI slop of what,
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  for example, deepfakes, audience mistrust, and AI generated
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  content does to the industry. But we also want to look into
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  tangible ways of how to maintain credibility, democratic
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  functions and navigating the rapidly changing technological
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  conditions. Hi, Paul.
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  Good morning. Good morning, Yifa, nice to be
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  here. I'm particularly intrigued to
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  dive into the intersection of tech, trust, and media with you
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  today. But before we start into the
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  topic particularly, I would love to hear your thought on the
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  intersection of tech and trust. What is the first thing that
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  comes to your mind when you hear that?
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  So I think like trust and tech isn't really about blind faith.
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  I think it's has a lot to do about understanding how the
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  systems work and and who's accountable basically.
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  So I think initially when especially tech is
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  transformative, people tend to mistrust the technology and a
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  lot of trust can be gained when you start working with it or
• 2:21
  just do like, yeah, do something with it and then you build up
• 2:25
  trust. You understand how the system
• 2:27
  works and then you also find out or figure out which components
• 2:32
  of the system you can trust and like which other parts of the
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  system maybe are a little bit more ambiguous and you have to
• 2:40
  double check things in this domain.
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  So I think it has a lot to do with understanding the
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  technology to basically build up trust for things that are new.
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  And the things that I knew, something that is like on your
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  daily program, so to say, because as head of AI at one of
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  the largest like media houses in Germany, you're basically at the
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  linchpin of like newness and media and also the technology in
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  itself of what shapes the industry.
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  And you also sit at the core of digital transformation.
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  So what is, according to your opinion and expertise of what
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  you saw over the past couple of weeks and months, really the
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  biggest misconception when we talk about AI and journalism
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  today? I think we have to kind of
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  differentiate between those within the field of media and
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  and those who are basically more or less consumers of media.
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  I would say that as people who are reading the news or
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  consuming media primarily, there is this belief that that the
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  media is like not putting a lot of thought into how they use AI.
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  So I think some of them might have like the the conception at
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  least that that media companies start using AI just to create
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  content with it in a very naive manner.
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  And I think part of this is also true for those who are in the
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  field or working as, as journalists or in, in media
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  companies that they initially think now the shift towards AI
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  means that we have to naively adopt the technology and create
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  just more content without supervision, without shaping
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  this how the system work. And I would say the reality is
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  more like that that now is a, is a time where you basically can
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  reshape the whole process of creating media, creating
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  content. And those who basically put a
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  lot of effort into it and, and know how to integrate the
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  technology well, they will succeed in a way that they will
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  not just create a lot of AI slop, let's put it like that,
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  but actually use AI to amplify also the quality of the media.
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  I think you can like if you do it right, you can, you can
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  integrate AI at the right steps in the process of creating
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  various kinds of content that actually do amplify by the final
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  results instead of like lowering or damping what comes out.
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  And media companies shouldn't like just naively adopt the
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  technology. They should focus on on this
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  part of figuring out how you can create good content that people
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  are interested in with the technology.
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  And you said like a word that does actually all over the news.
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  It's like the AI slop. Well, does that mean to you
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  particularly? And also, could you explain a
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  little bit more of how it impacts your work or also
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  another work of like the media house that you're part of?
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  Yeah, I mean, AI slob, I kind of dislike the word a little bit,
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  but it it's just around there and people use it to describe, I
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  would say mediocre or bad content that is just created
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  with AI tools. Could be a short video for
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  social platforms could be news articles that are published on
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  pages that are not really trustworthy.
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  So this is typically the kind of content that we refer to with AI
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  slop, but also sometimes you can kind of like put this as a label
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  on a lot of different kinds of content, and sometimes you're
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  also not really appreciating certain kinds of AI content that
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  are actually good. I mean, I just recently talked
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  about the the cat that is playing various instruments on
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  on the front porch. And I actually believe this is
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  this is a unique kind of quality content.
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  Actually, while it is completely created by by AII think it's
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  VO3A video model that created these scenes where where a cat
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  is playing. I don't know, a lot of different
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  kinds of instruments, which obviously you could never film
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  because a cat would never do this.
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  But it's really entertaining to to see this kind of idea and
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  it's funny and, and it has a lot of things that that people would
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  usually put into content when they created.
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  So I think we have to be careful with the AI slot label.
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  But on the other hand, I think it really good, nicely describes
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  how consumers of media are now have a hard time navigating the
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  landscape of media and basically figuring out what is trustworthy
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  and what is not trustworthy. And it surfaces this challenge
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  that AI content gives us to basically also judge what is
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  true, what is not true, what can I believe and what is just like
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  fake news. And I think there are two sides
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  of like the fear that people have.
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  So on the one hand side, it's the fear of trusting the media
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  output and contents in terms of information that you consume.
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  And on the other side, it's also like losing the trust in
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  journalism overall and the craft of journalism and in itself,
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  because also there is a lot of talks that journalists won't
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  have a job. If you just would like put it
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  really like frankly, and that the quality is erode.
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  What do you see AI actually enabling, particularly when you
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  talk about quality or quantity? And where do you see legitimate
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  risks that could impact the way of how we the news that we
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  consume? I think AI, like in The
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  Newsroom, is really good at automating tedious tasks, tasks
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  that nobody really likes but you have to do as a journalist.
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  So transcribing things, summarizing things, doing the
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  research for your papers. AI can assist with a lot of
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  these processes and basically can give you more time to focus
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  on those parts of the process that should always be done by a,
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  by a human, by by somebody who is crafting the content and who
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  is in in the driver's seat for the final result.
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  So this is also something that we at for Comedian Corpora
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  inherently believe in that it is really important to keep the
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  human the center of the process at the beginning and the end.
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  So whenever content is created, a human should be the one who's
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  deciding how to create it. What, what message do you want
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  to convey? And also how should the final
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  piece look? And, but obviously AI can help
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  us to be a lot more efficient. And that's something that we
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  should appreciate and, and integrate.
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  But then I mean, on the other hand, we should always focus
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  that we will keep some things with the human in the loop.
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  And then you also asked about like trust, How can we keep the
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  trust basically with us? And I would say in the situation
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  right now where you have a lot of AI slot and users have
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  trouble like navigating to landscape brands and especially
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  also people become like very important to basically build up
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  trust. Because trust in media also for
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  me means to build up a relationship with either people
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  or it could also be a relationship to to a news brand
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  where you basically know that they work thoroughly.
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  And I think for media companies, this is kind of like something
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  that they should put on their top priority.
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  We have to like make sure that we always keep the trust with
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  our audience, that we keep this relationship to our audience,
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  and be really transparent about how we use AI to basically make
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  sure that we are the ones that people go to if they want to
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  know that they can trust us with how we talk about what's
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  happening in the world. If we talk transparency, it's
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  one of like the most crucial parts of of the whole discussion
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  of how we set up and interact with AI, particularly in the way
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  that content is shaped. Who do you think is responsible,
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  the consumer or the provider or like a third party organization
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  that provides, you know, frameworks for it?
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  I think it should be a shared responsibility that is with
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  everybody who's every party that is part of the process of
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  shaping or creating content. I think the those who trained
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  the big AI models, they, they could obviously do a lot more in
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  terms of transparency. We still don't really know which
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  data went to went into the models, into the big LLMS that
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  were trained. So we need a lot more
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  transparency about the training data.
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  But then also us as intermediaries who are using
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  this technology and either incorporate this into workflows,
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  we need to make sure that we be that we are transparent about
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  how we use it. And for instance, if we like, we
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  have a certain set of rules, for instance, that if something is,
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  is, it's created by AI. And then we, we label this piece
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  of content that is an AI created piece.
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  And we also make transparent about like what, what, what part
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  of this is created by an AI. So to give you an example, we
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  have a daily newsletter where people can now also listen to
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  the newsletter. And the newsletter is written by
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  one of the journalists in our newsroom.
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  And now they can hear the newsletter that is is an audio
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  spoken by the person who wrote the newsletter.
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  But we use AI to basically turn the text into audio.
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  So we use 11 apps here to basically create an audio
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  podcast for the newsletter. And this is really good because
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  we basically reach our readers or listeners in this case in
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  another type of media. And we can like transform the
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  original content piece into something else with very little
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  effort. We can use AI for this, but we
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  make sure that this is transparent and that that the
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  the the user knows like what is happening.
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  What's going on here? I think that is so like tangible
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  of how you incorporated it into your processes or the way that
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  you approach AI. From my experience, it's hard to
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  be that clear as the way that you also just reflected upon of
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  how you as an organization are. How did you get there?
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  Like how did you come up with this clarity of how you use AI
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  and who did you also loop into the preparation to get there?
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  Yeah, I think that's a, that's a, that's a big challenge
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  actually for for media companies, probably also for
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  companies in other domains because you AI is basically
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  everywhere and it it has they're like every part in your company
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  basically is more or less involved into this process of
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  agreeing on let's say a certain consensus how to use AI in the
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  company. And so, so in our case, we
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  basically had to make sure that we getting agreement between the
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  Workers Union, the, the, the high level executive board,
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  those who work in the unused room and, and, and basically
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  make sure that every party is involved in this process and can
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  like incorporate their perspective on this.
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  And then basically, you know, try to, to, to agree on
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  something certain set of rules that we all think are valid.
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  But also to be honest, I think because the technology is just
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  evolving so rapidly, this is not really for a long duration and
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  you basically always have to refine these things.
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  If you just think like how much has happened in this year alone
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  and also how much has the industry moved?
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  How much more are we like moving towards AI content?
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  If you look at video, for instance, like video AI at the
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  beginning of the year wasn't really a thing that you like,
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  think is is around there. But, but now it's like
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  everywhere on social media, you can find video AI.
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  So that just shows us like how fast things are moving and that
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  you constantly need to like re evaluate also the, the, the set
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  of consensus rules or points that you agree on and and make
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  sure that you're always in line with how the technology evolves.
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  But doesn't that make you like not only the head of AI, but
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  more of an in like integrator, orchestrator between the
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  different kinds of, you know, skill sets, stakeholders?
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  I mean, how you just walked us through of who's like needed to
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  be looped in. That's like quite the pool of
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  people that could form an opinion or so how do you
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  navigate that? Yeah, it's, it's really, it's
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  really crazy to basically have all these hats on and, and, and
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  shift between them. So basically in between a tech
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  lab that I'm also managing. And then basically this, this
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  role, which is more like an internal, I don't know, policy
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  team or whatever, who is trying to put every or get everybody on
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  the table. I think, I think this is just
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  part of the role. And also what I, what I see in
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  the industry is that this is an emerging role that many
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  companies now have. And there are people in other
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  companies who have similar challenges.
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  And, and I think it's, it's, it's just necessary.
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  Somebody needs to like, like connect the dots and make sure
• 17:09
  that the Dai topic is just steered in the right way.
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  And, and this is you, you cannot like the, the, the topic is just
• 17:19
  so big. You cannot, like, you cannot do
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  it this on your own. You cannot just like make
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  decisions on your own. You have to get everybody
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  involved. So it's it's just you have to do
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  it this way or I think it will fail, you know?
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  And when you now build or deploy the AI systems with the lab that
• 17:37
  you have also the, you know, partnerships that you form, what
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  are key decisions of where like transparency, ethics and like
• 17:47
  the technological infrastructure that's needed collide?
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  So, yeah, so I think there are like obviously like several
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  layers of or at several layers, you basically have to integrate
• 18:04
  safety and also make sure that the system basically responds in
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  the way that is aligned with how you want it to respond and also
• 18:16
  make sure that your ethical standards are met.
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  I think on a very low level, you basically have like the
• 18:26
  decisions for the the models that you use in your system.
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  So basically we we make sure that we use models that we think
• 18:34
  put a lot of effort into meeting ethics standards and were
• 18:40
  transparent about, you know how they are at least a little bit
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  more transparent about how they train their models.
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  And also that they put efforts into making sure that these
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  models to at least to certain extent comply with some ethical
• 18:58
  standards. So like we're not like
• 19:01
  triggering hate speech or harmful content.
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  So that's on the on the model base where you basically select
• 19:10
  certain types of model that you want to integrate into your
• 19:13
  systems. And then basically on top of
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  that, you just build up the system.
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  So, and then it really depends on like what kind of system you
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  built. Do you build something for
• 19:22
  internal use that is basically, I don't know, drafting something
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  or used in The Newsroom for like research purpose or for giving
• 19:34
  you feedback on, on your article or anything like that.
• 19:39
  Then we have different constraints and then we
• 19:41
  basically can do like some internal testing.
• 19:43
  We have feedback loops, these kind of things.
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  We also have the human in the loop principles.
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  So everything that where AI is used will always be reviewed by
• 19:52
  human. And then we have other systems
• 19:55
  where we basically built AI systems that are user facing.
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  So when users interact with AI systems, you cannot really make
• 20:04
  sure that every answer is proof read by humans.
• 20:07
  So basically then you have to double check that the system is
• 20:11
  working. Accordingly, so we do intensive
• 20:14
  red teaming. So basically we try to to get
• 20:20
  like the worst out of the bot, so to speak.
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  So we're trying to trick the bot into giving us answers that the
• 20:27
  bot shouldn't give us. We're trying to jailbreak the
• 20:30
  bot in a way and only if like this red teaming and and several
• 20:36
  other tests are positive, then we are rolling the system out to
• 20:40
  the users. And then even if we rolled the
• 20:43
  system out, we always make sure that we have feedback loop and
• 20:47
  can check, check that the quality is good.
• 20:54
  So yeah, several levels. Yeah.
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  I mean, that's amazing. And I think particularly when
• 20:59
  you talk about the quality and coming back also to the craft
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  part of like output in itself or in the way that like news are
• 21:10
  being brought to the market. Basically, we live in an era
• 21:13
  where deep fake has never been that easy to produce.
• 21:16
  And also like synthetic content that is actually not real but
• 21:21
  artificial like logically, but is supposed to be hyper
• 21:27
  personalized. It's also supposed to be in real
• 21:29
  time. But still, there's this like
• 21:31
  underlying feeling that I cannot trust of what I see in the news
• 21:37
  that I consume anymore. And where is the tipping point
• 21:41
  at which, like AI begins to distort reality more than it
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  supports it for you and also from your experience in the
• 21:50
  field? I think we, we already reached a
• 21:55
  point where this is like really a topic that we should take
• 21:59
  seriously on several levels, also on our societal level.
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  And we need to like bring these discussions to the table.
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  There are studies that are showing that the majority of
• 22:12
  content that is now being published online is AI created.
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  And from like anecdotal consumption of social media, you
• 22:22
  can really see that there are a lot of instances where I'm not
• 22:26
  able or where at at least I takes me several minutes to
• 22:30
  basically think about if videos are real or not.
• 22:33
  And, and I can't really tell. And these are obviously only the
• 22:37
  examples that trigger this. There might be a lot of videos
• 22:41
  that I see that are AI created and I'm I'm, I just don't
• 22:46
  realize it anymore. So so I think this is really
• 22:50
  really an important topic that we need to bring to the table
• 22:54
  also to be discussed by our politicians, by those who shape
• 22:59
  basically the guardrail. It's the, the, the, the laws
• 23:03
  also for, for how basically the media landscape is, is created
• 23:07
  because I think ultimately also like our democracy needs that we
• 23:12
  have trustworthy media that basically is, is helping the
• 23:20
  people in the country to shape their opinions in a way that
• 23:25
  they're not just influenced by AI content.
• 23:28
  And if we look at a very at the political, like the global
• 23:34
  political landscape right now, there are many actors that can
• 23:38
  definitely use AI for purposes that are very bad and that can
• 23:44
  really harm our society. So, so this is not, not
• 23:49
  something that maybe we need to talk about.
• 23:54
  It's this is a serious topic that we need to talk about.
• 23:57
  And I think like for us as a media company, we also have a
• 24:01
  responsibility to, to, to participate into mitigating the
• 24:06
  situation and basically helping people to build trust.
• 24:10
  And I think for us this, this like, I mean, we have to
• 24:14
  basically fight against this huge amount of synthetic
• 24:22
  content. We have to be visible.
• 24:25
  We have to convince our readers that we are trustworthy every
• 24:31
  day again and again and build up our communities and our
• 24:35
  audiences. I think we, we put a lot of
• 24:37
  effort into, in into doing this and, and this like to, to be, to
• 24:44
  be relevant in the future means to basically use AI but use it
• 24:48
  in a good way and also show others that AI is it's, it's
• 24:56
  not, it's not ultimately bad. It's, it's also, it's, it's in a
• 24:59
  way also like tool. So if you're a bad actor and use
• 25:02
  a tool for bad purposes, it can be very harmful.
• 25:05
  But if you are somebody who tries to build up trust and be
• 25:09
  trustworthy, you can also use AI.
• 25:11
  But you have to make sure to use it in a transparent way.
• 25:14
  Because then we can basically compete with this AI slot that
• 25:19
  is out there and be visible and show users that we have
• 25:25
  trustworthy information that they can consume.
• 25:29
  And if you now think about like the way that you work, it's
• 25:33
  mostly like summarized under the like newsroom that you like are
• 25:40
  sometimes even literally in the room with.
• 25:43
  And the way that you just reflected upon the impact of
• 25:47
  trustworthy news versus like the fake news and the way that also
• 25:52
  media like companies need to bring this to the consumer.
• 25:58
  Does it have an impact on The Newsroom in an AI world, so to
• 26:04
  say? And if so, if you yourself could
• 26:08
  redesign a newsroom today for like the future, how would you
• 26:13
  like? How would you set it up?
• 26:15
  Why and how to get there? That's a really good question.
• 26:22
  Yeah, I think that there could be a whole podcast episode in in
• 26:25
  itself. So maybe I'll focus on on two
• 26:28
  points. I think.
• 26:29
  I think one truth that a lot of media companies still struggle
• 26:35
  with is that news is inherently shifting from text to other
• 26:41
  forms. It's more today, it's more about
• 26:44
  audio, it's podcasts, it's video on social media platforms that
• 26:51
  are consumed as a news source, especially by younger
• 26:54
  generations. And we have to acknowledge this.
• 26:58
  And I think in newsroom of the future would be a lot more video
• 27:05
  first, podcast 1st and article second, I would say.
• 27:13
  So that's one thing. And then also maybe the other
• 27:16
  thing would be to also, yeah, design the The Newsroom AI
• 27:24
  first. Because as you can imagine,
• 27:27
  there are people who have been in journalism for 20 or 30 years
• 27:34
  and they have a typical way how they, how they work, how they,
• 27:39
  how they structure the work. And I think if you look at young
• 27:44
  startups, for instance, at Morning Crunch, which is a
• 27:49
  newsletter first news company, they build up a newsroom that is
• 27:59
  basically AI first. So, and what that means
• 28:03
  basically is to rethink every component of the process in
• 28:08
  terms of where can we maximize the efficiency of AI for this
• 28:13
  process without compromising any of the quality.
• 28:17
  And I mean, obviously it's always easy to design this on
• 28:21
  Greenfield and it's much more difficult to basically transform
• 28:31
  legacy media into a new newsroom.
• 28:34
  But but yeah, maybe maybe two things that I would that I would
• 28:38
  emphasize is to, to go on on something like video 1st or the
• 28:41
  1st and then basically create a create a more streamlined AI
• 28:47
  first process of of how to work in The Newsroom.
• 28:52
  And I like that you are so like specific about the forms of
• 28:58
  content and like how it depends on the like, generations,
• 29:02
  consumption, also the habits that we've formed.
• 29:05
  But doesn't that also inherently mean that we need to think about
• 29:08
  distribution of content? So currently, I would almost say
• 29:14
  everyone's consuming media through social media and like on
• 29:20
  a, let's say classical way that you go to a website or app of a
• 29:25
  specific like news house. However, if we I now understand
• 29:30
  you correctly, you also propose that the way that we like
• 29:34
  consume it via the distribution channels will massively change
• 29:39
  because the forms of contents change.
• 29:42
  Or how would you also view that of like a prerequisite for media
• 29:51
  houses to link it to the production?
• 29:55
  And how would you then also regard it to be enabled through
• 30:00
  AI or maybe also like challenged by AI because it's not possible
• 30:06
  to for whatever reason? Yeah, I think, I think the
• 30:13
  distribution also matters. In an ideal world, and I would
• 30:17
  say like in a especially in a world before the introduction of
• 30:21
  Cha Chi PT and Google AI overviews, Google AI mode, these
• 30:25
  kind of things, we always as publishing houses after the
• 30:31
  introduction of Internet, we were aiming for having direct
• 30:34
  relationships to our consumers and basically making sure that
• 30:38
  people will find us with relevant keywords that they
• 30:42
  search for and then basically click on a link and then they
• 30:45
  are on our website. What we see now today is that
• 30:50
  this is becoming increasingly difficult and more and more
• 30:54
  difficult basically because this this old contract of the
• 31:00
  Internet, the search contract that you basically get referral
• 31:06
  clicks for your content. This is not not really working
• 31:09
  anymore. We see that ChatGPT and Google
• 31:13
  AI overviews are having significantly lower click
• 31:17
  through rates and referral traffic.
• 31:19
  So, so it's becoming harder and harder to basically get people
• 31:23
  on your, on your websites. And, and this is also true for,
• 31:31
  for, for social media because it's, it's really difficult to,
• 31:35
  to get out of there and, and, and the platforms make sure to
• 31:38
  build up this water garden basically that's keeping people
• 31:41
  inside the, the platform. However, I think it's, it's
• 31:47
  still relevant to basically try getting people onto your
• 31:52
  websites and to your products and to build up direct
• 31:55
  relationships with them. I think there are a lot more
• 32:00
  opportunities that media companies now explore.
• 32:02
  This could be live events, this could be, I don't know,
• 32:07
  mailings. This could be WhatsApp channels
• 32:09
  that are like directly connected to your journalists and editors.
• 32:13
  So basically like I think in the core is to build up this
• 32:18
  relationship between you and and your readers and your audience
• 32:23
  and make the best of how this how the the media landscape is
• 32:27
  right now. And then I mean, on another
• 32:30
  level, I think, and this is more on, on the political level, I
• 32:35
  think we have to make sure to basically have a media landscape
• 32:39
  that is, that is taking into account to basically have a, a
• 32:46
  level playing field and have a, have a fair, fair market where
• 32:54
  basically users are still able to, to get to, to the sources
• 33:00
  and, and to get to the publishing companies.
• 33:02
  Because as as a citizen of the country, I I strongly believe we
• 33:07
  need independent media and we have to make sure that this that
• 33:15
  this market still exists and will exist in the future.
• 33:19
  And sorry, I I need to go. That was like a jump off point
• 33:23
  for me into one of my passion topics, independent media.
• 33:28
  And you linked it to because I'm a citizen of this country.
• 33:32
  So you inherently linked like independent media to the
• 33:36
  political landscape. And one key question that I
• 33:40
  always have myself is how do we get there?
• 33:44
  What would you propose? That's a.
• 33:48
  That's a good question. If I would have the solution,
• 33:52
  I'd probably have a. Different head on your head.
• 33:57
  Yes, that would be really great if if somebody had the solution.
• 34:03
  No, I think, I think, I think we have to at one point we have to
• 34:08
  fight for like for, for a European standpoint and the
• 34:12
  whole ecosystem because a lot of the media landscape today is
• 34:17
  also shaped by big tech companies from the US and also
• 34:22
  China. So basically we have to
• 34:24
  basically build up our own standpoint.
• 34:27
  This doesn't really mean to like cut off every connection to
• 34:33
  them, but to basically find an agreement.
• 34:37
  And I think, I think as I mentioned, the technology's
• 34:44
  evolving so rapidly and I, I think like within this journey
• 34:47
  also, I myself have like change my perspective on certain
• 34:53
  things. And, and also on this question,
• 34:56
  I would, I'm, I'm still a little bit optimistic that that I think
• 35:01
  there is the potential that after a very rapid and
• 35:05
  disruptive introduction of AI platforms like Chechi PT or also
• 35:11
  the new AI summaries in Google, that we now can basically get
• 35:17
  back to the table and negotiate with those companies also on
• 35:22
  different levels, politics, the media companies and everybody
• 35:26
  who was involved to basically find solutions that work for
• 35:30
  everybody. Because I think in the end, this
• 35:36
  experience shift that we observed with AI that was
• 35:40
  introduced by chair GPT mainly and now is you can see in many
• 35:44
  platforms, this is not going away.
• 35:46
  I think because as a consumer, I love, I love this.
• 35:49
  It's really, really nice way of getting information in a very
• 35:53
  condensed way. And now we just have to
• 35:56
  basically figure out how we can work with this new technology in
• 36:01
  a way that independent media can still survive.
• 36:07
  So right now I already have at least two another episodes that
• 36:11
  I would want to do to the like first, The Newsroom of the
• 36:14
  Future and 2nd Independent Media.
• 36:16
  How do we get there? But for now, we're almost at the
• 36:21
  end of this one. So I do want to cherish the
• 36:24
  moment and have some so-called in between moments with you.
• 36:28
  They are intuitive ones. And these are the ones where you
• 36:32
  literally just share what comes to your mind 1st.
• 36:36
  And I do want to start with like a key question for me to you,
• 36:40
  what does trust and media feel like?
• 36:44
  I would say it feels like a handshake that must be renewed
• 36:48
  every single day. That is such a great visual.
• 36:55
  Let's see how we can include that.
• 36:56
  Anyways, what should I never do? I think it should never make
• 37:03
  decisions that are for good reason, still in the hands of
• 37:07
  people, like hiring people for instance.
• 37:10
  And what will journalists always do?
• 37:14
  I think journalists will always provide the context, they will
• 37:18
  bring in the empathy and also their very own and unique
• 37:23
  standpoint on how things are in the world.
• 37:29
  What is the future of news? To make producing news very
• 37:38
  efficient and high quality thanks to AI with the very
• 37:43
  thorough overview of human experts.
• 37:50
  And do you have an experience that you yourself are currently
• 37:54
  in between? Yeah, I think in between.
• 38:02
  I think I'm I'm always in between hype and reality when it
• 38:07
  comes to AI because sometimes we're just thinking the whole
• 38:12
  world change and then in the reality it's maybe not as big as
• 38:16
  we think. And if we now close the session
• 38:23
  and finish it with just a link to take and trust topic, and if
• 38:29
  we if we assume that trust becomes a decisive competitive
• 38:34
  advantage in media, what does it mean in your role to be a trust
• 38:38
  builder when it comes to the future of media news?
• 38:44
  I think to, to build up trust is it is really important to, to be
• 38:53
  very transparent inside the company and also to your
• 38:58
  audience about how you want to use AI, how you use it, what AI
• 39:02
  is, what it can and what it can't.
• 39:06
  And to basically for me like what is really at my heart to
• 39:11
  everybody at Funker, to everybody who works at the
• 39:14
  company, give them the opportunity to understand this
• 39:18
  technology as good and as deep as they want, give them all the
• 39:22
  resources. Because I think ultimately, and
• 39:25
  this basically links back to what I said in the beginning, I
• 39:29
  think trust is built on experience and understanding of
• 39:33
  technology. Thank you so much for your time
• 39:38
  and diving so deep into how you had fungus set up, AI overall,
• 39:44
  but also how you view it for the industry.
• 39:48
  It was super valuable conversation.
• 39:50
  It was great having you. Thanks so much.
• 39:53
  Thanks Yvonne, it was fun.