AI-Washing: Whoever Has No Moral Compass Now Will Never Build One

At first glance, many media companies have tackled the subject of AI in an almost exemplary manner. They got off to a flying start, experimented, formed interdisciplinary teams, appointed AI directors and – after all, they do have a responsibility – developed ethical guidelines. Of course, the industry was also able to express outrage at a few moral outliers – “Sports Illustrated!”, “the Michael Schumacher interview!”, “Burda’s cookery magazine!” – but in many places, lengthy lists of “dos and don’ts” were intended to prevent the worst. It’s just a pity that most of these sets of rules are likely to prove, before long, to be a form of AI-washing. For they give the illusion of control that has long since slipped from the media companies’ grasp. To paraphrase a line from Rainer Maria Rilke‘s famous poem „Autumn Day“: Whoever has no moral compass now will never build one.

The long version is this: rapid technological progress and the power of tech conglomerates, coupled with economic and, in some places, political pressure, have created realities that even exemplary management finds difficult to cope with. This is suggested by research for the EBU News Report “Leading Newsrooms in the Age of Generative AI“, published in 2025.

First and foremost, there is the issue of ‘shadow AI’. One media manager observes that the biggest changes are not currently being driven by media organisations, but are arising simply because journalists are using AI tools. Unlike 25 years ago, when editors and reporters still had to be painstakingly convinced of the value of digital tools and platforms, AI tools are so intuitive to use that people employ them even more frequently in their private lives than at work, as a recently published study found – at least with regard to the US. A report by the Thomson Reuters Foundation supports this view for journalists in the Global South: 80 per cent of those surveyed used AI at work, but not even 20 per cent of their newsrooms had a corresponding strategy or policy in place. Yet within organisations, both groups can become a problem: the tech-savvy staff who overstep boundaries whilst experimenting, and the less tech-savvy staff who make mistakes out of ignorance, such as disclosing sensitive data.

Added to this is the fact that many ethical guidelines are not practical for day-to-day use. The BBC is no doubt proud that it has condensed its current guidance on AI into just nine points – plus sub-points. But one cannot expect editors, who are under such time pressure during their shifts that some do not even dare to go to the loo, to have internalised all the regulations. The workload is likely to push journalists even more towards using AI. Just as text from dpa reports has occasionally had to be used in the past, an LLM will be consulted in future if it saves time.

The rule that is currently most widely followed is particularly difficult to adhere to: ‘Human in the loop’ – a human should have the final say on AI-generated content before it is published . Even in day-to-day operations, editors overlook errors. When AI tools multiply the speed of output, humans reach their cognitive limits. And if they were to work meticulously nonetheless, they would inevitably prevent the efficiency gains hoped for by management. The ‘human in the loop’ principle undermines the scalability that is expected from AI, writes Felix Simon in a commentary for the Reuters Institute.

When in doubt, editorial teams wriggle out of the requirement with disclaimers. They state that content translated or produced using AI is labelled as such – in other words: they accept no responsibility for errors. This can work well and is accepted by the audience in some cases, for example with subtitles for TV programmes. Here, the desire for comprehensibility takes precedence, for instance for the hearing-impaired. However, it can also produce rubbish, as with articles from the Washington Post that are translated by AI and published in the Ippen Group’s publications. Furthermore, even in high-quality journalism, there are scenarios where rigid rules are of no help. If, for example, cloned voices were banned across the board, it would limit narrative possibilities. Even public service broadcasters have used voice clones of historical figures to bring contemporary history to life.   

In all these cases, guidelines tucked away on the intranet are of no help. A more effective approach is a mix of technical solutions, training and debate: desired applications must be automated within the CMS. Through experiments and training sessions, users can acquire knowledge of AI and learn how to work with it. And those who regularly discuss values – even in highly contentious cases – are more likely to reflect on them and act accordingly. Monitoring staff at every stage of research and production has never worked in journalism. Each individual must calibrate their own moral compass and follow it.

However, this is of little use if the management doesn’t have one. The owner of the Los Angeles Times, for example, recently ordered the editorial team to tag comments using a tool called the Bias Meter. The AI automatically alerts readers to opposing viewpoints. Clearly, the output isn’t being proofread by humans. Otherwise, someone might well have noticed that the machine had cast the Ku Klux Klan in a somewhat too favourable light. But those are details. No AI tool will ever be able to replace a lack of trust. Yet using AI in this way can destroy hard-won trust – both amongst staff and with the public.

What could, however, render ethical guidelines in media organisations entirely obsolete is the dominance of tech giants. The more AI is embedded in all the tools that everyone uses in their daily lives, the less users will question the values underlying them. The fact that a smartphone camera always makes the sky a little bluer than the one you see in front of you – fair enough. The fact that search engines make answers increasingly easy to digest thanks to AI – that’s fine. The fact that word processing programmes are increasingly acting as editors before an editor has even seen the piece – why not? Only the major organisations will be able to afford to embed their own standards in their systems, and even those may well be infiltrated by AI. The rest will be working with Office and the like.

None of this has to be a bad thing. Autopilots have made air travel many times safer; autonomous driving will achieve the same on the roads. Perhaps AI-assisted journalism will also manage to raise the standard of overall output significantly. After the devastation of the ‘reach’ era, that’s not such a difficult task for some publications. AI tools may even eventually help to implement ethical guidelines. The crucial question is whose rules these will be.

This column was published by the German industry publication Medieninsider in German on 17 March 2025.#

Prof. Pattie Maes, MIT: “We don’t have to simplify everything for everybody”

Prof. Pattie Maes and her team at the MIT Media Lab conduct research on the impact of generative AI on creativity and human decision-making. Their aim is to advice AI companies on designing systems that enhance critical thinking and creativity rather than encourage cognitive offloading. The interview was led for the upcoming EBU News Report “Leading Newsrooms in the Age of Generative AI”.  

It is often said that AI can enhance people’s creativity. Research you led seems to suggest the opposite. Can you tell us about it?  

You’re referring to a study where we asked college students to write an essay and had them solve a programming problem.  

We had three different conditions: One group could use ChatGPT. Another group could only use search without the AI results at the top. And the third group did not have any tool.  

What we noticed was that the group that used ChatGPT wrote good essays, but they expressed less diversity of thought, were more similar to one another and less original. 

Because people put less effort into the task at hand? 

We have seen that in other experiments as well: people are inherently lazy. When they use AI, they don’t think as much for themselves. And as a result, you get less creative outcomes.  

It could be a problem if, say, programmers at a company all use the same co-pilot to help them with coding, they won’t come up with new ways of doing things.  

As AI data increasingly feeds new AI models, you will get more and more convergence and less improvement and innovation.  

Journalism thrives on originality. What would be your advice to media managers? 

Raising awareness can help. But it would be more useful if we built these systems differently.  

We have been building a system that helps people with writing, for example. But instead of doing the writing for you, it engages you, like a good colleague or editor, by critiquing your writing, and occasionally suggesting that you approach something from a different angle or strengthen a claim.  

It’s important that AI design engages people in contributing to a solution rather that automating things for them.  

Sounds like great advice for building content management systems. 

Today’s off-the-shelf systems use an interface that encourages people to say: “write me an essay on Y, make sure it’s this long and includes these points of view.”  

These systems are designed to provide a complete result. We have grammar and spelling correctors in our editing systems, but we could have AI built into editing software that says, “over here your evidence or argument is weak.”  

It could encourage the person to use their own brain and be creative. I believe we can design systems that let us benefit from human and artificial intelligence.  

But isn’t the genie already out of the bottle? If I encouraged students who use ChatGPT to use a version that challenges them, they’d probably say: “yeah, next time when I don’t have all these deadlines”.   

We should design AI systems that are optimised for different goals and contexts, like an AI that is designed like a great editor, or an AI that acts like a great teacher.  

A teacher doesn’t give you the answers to all the problems, because the whole point is not the output the person produces, it is that they have learned something in the process.  

But certainly, if you have access to one AI that makes you work harder and another AI that just does the work for you, it is tempting to use that second one. 

Agentic AI is a huge topic. You did research on AI and agents as early as 1995. How has your view on this evolved since? 

Back when I developed software agents that help you with tasks, we didn’t have anything like today’s large language models. They were built by hand for a specific application domain and were able to do some minimal learning from the user.  

Today’s systems are supposedly AGI (artificial general intelligence) or close to it and are billed as systems that can do everything and anything for us.  

But what we are discovering in our studies is that they do not behave the way people behave. They don’t make the same choices, don’t have that deeper knowledge of the context, that self-awareness and self-critical reflection on their actions that people have.  

A huge problem with agentic systems will be that we think they are intelligent and behave like us, but that they don’t. And it’s not just because they hallucinate. 

But we want to believe they behave like humans? 

Let me give you an example. When I hired a new administrative assistant, I didn’t immediately give him full autonomy to do things on my behalf.  

I formed a mental model of him based on the original interview and his résumé. I saw “oh, he has done a lot of stuff with finance, but he doesn’t have much experience with travel planning.” So when some travel had to be booked, I would tell him, “Let me know the available choices so that I can tell you what I value and help you make a choice.”  

Over time my mental model of the assistant develops, and his model about my needs and preferences. We basically learn about each other. It is a much more interactive type of experience than with AI agents.  

These agents are not built to check and say, “I’m not so confident making this decision. So, let me get some input from my user.” It’s a little bit naïve that AI agents are being portrayed as “they are ready to be deployed, and they will be wonderful and will be able to do anything.”  

It might be possible to build agents that have the right level of self-awareness, reflection and judgment, but I have not heard many developers openly think about those issues. And it will require a lot of research to get it right.  

Is there anything else your research reveals about the difficulties with just letting AI do things for us? 

We have done studies on decision making with AI. What you expect is that humans make better decisions if they are supported by an AI that is trained on a lot of data in a particular domain.  

But studies showed that was not what happened. In our study, we let people decide whether some newspaper headline was fake news or real news. What we found was when it’s literally just a click of a button to get the AI’s opinion, many people just use the AI’s output.  

There’s less deep engagement and thinking about the problem because it’s so convenient. Other researchers got similar results with experiments on doctors evaluating medical diagnoses supported by AI, for example. 

You are telling us that expectations in AI-support are overblown? 

I am an AI optimist. I do think it is possible to integrate AI into our lives in a way that it has positive effects. But we need to reflect more about the right ways to integrate it.  

In the case of the newspaper headlines we did a study that showed that if AI first engages you in thinking about a headline and asks you a question about it, it improves people’s accuracy, and they don’t accept the AI advice blindly.  

The interface can help with encouraging people to be a little bit more mindful and critical.  

This sounds like it would just need a little technical fix.  

It is also about how AI is portrayed. We talk about these systems as artificial forms of intelligence. We constantly are told that we’re so close to AGI. These systems don’t just converse in a human-like ways, but with an abundance of confidence.  

All of these factors trick us into perceiving them as more intelligent, more capable and more human than they really are. But they are more what Emily Bender, a professor at the University of Washington, called “stochastic parrots”.  

LLMs (large language models) are like a parrot that has just heard a lot of natural language by hearing people speak and can predict and imitate it pretty well. But that parrot doesn’t understand what it’s talking about.  

Presenting these systems as parrots rather than smart assistants would already help by reminding people to constantly think “Oh, I have to be mindful. These systems hallucinate. They don’t really understand. They don’t know everything.”  

We work with some AI companies on some of these issues. For example, we are doing a study with OpenAI on companion bots and how many people risk becoming overly attached to chat bots.  

These companies are in a race to get to AGI first, by raising the most money and building the biggest models. But I think awareness is growing that if we want AI to ultimately be successful, we have to think carefully about the way we integrate it in people’s lives.  

In the media industry there’s a lot of hope that AI could help journalism to become more inclusive and reach broader audiences. Do you see a chance for this to happen? 

These hopes are well-founded. We built an AI-based system for kids and older adults who may have trouble processing language that the average adult can process.  

The system works like an intra-language translator – it takes a video and translates it into simpler language while still preserving the meaning.  

There are wonderful opportunities to customize content to the abilities and needs of the particular user. But at the same time, we need to keep in mind that the more we personalize things, the more everybody would be in their own bubble, especially if we also bias the reporting to their particular values or interests.  

It’s important that we still have some shared media, shared news and a shared language, rather than creating this audience of one where people can no longer converse with others about things in the world that we should be talking about. 

This connects to your earlier argument: customisation could make our brains lazy.  

It is possible to build AI systems that have the opposite effect and challenge the user a little bit. This would be like being a parent who unconsciously adjusts their language for the current ability of their child and gradually introduces more complex language and ideas over time.  

We don’t have to simplify everything for everybody. We need to think about what AI will do to people and their social and emotional health and what artificial intelligence will do to natural human intelligence, and ultimately to our society.  

And we should have talks about this with everybody. Right now, our AI future is decided by AI engineers and entrepreneurs, which in the long run will prove to be a mistake. 

The interview was first published by the EBU on 1st April 2025.

Trusted Journalism in the Age of Generative AI

Media strategist Lucy Küng regards generative AI as quite a challenge for media organizations, particularly since many of them haven’t even yet mastered digital transformation to the full extent. But she also has some advice in store: “The media industry gave away the keys to the kingdom once –  that shouldn’t happen again”, she said in an interview led for the 2024 EBU News Report “Trusted Journalism in the Age of Generative AI”. Ezra Eeman, Director for Strategy and Innovation at the Netherland’s public broadcaster NPO, thinks that media organizations have a moral duty to be optimists around the technology. It will increase the opportunities for them to fulfill their public service mission better. These are just two voices, many more are to come. 

The report that is based on about 40 extensive interviews with international media leaders and experts will discuss the opportunities and risks of generative AI with a special focus on practical applications, management challenges, and ethical considerations. The team of authors includes Felix Simon (Oxford Internet Institute), Kati Bremme (France Television), and Olle Zachrison (Sveriges Radio), Alexandra is the lead author. In the run-up to and following publication, the EBU will publish some interviews. They will be shared here:

Nic Newman, Senior Research Associate, Reuters Institute: “Transparency is important, but the public does not want AI labels everywhere“, published on 28th June 2024.

Sarah Spiekermann, Professor WU Wien: “We need to seriously think about the total cost of digitazation“, published on 13th June 2024. 

Kai Gniffke, Director General SWR, Chair ARD: “AI is an incredible accelerator of change ..It’s up to us to use this technology responsibly“, published on 3rd June 2024.

Jane Barrett, Global Editor at Reuters: “We have to educate ourselves about AI and then report the hell out of it“, published on 16th May 2024. 

Ezra Eeman, Strategy and Innovation Director NPO, “We have a moral duty to be optimists“, published on 17th April 2024.  

Lucy Küng, independent Media Strategist: “The media industry gave away the keys to the kingdom once – that shouldn’t happen again“, published on 27th March 2024.