Beyond the Deals: Unpacking the AI Revolution in Media

The Unseen Impacts, Ethical Quandaries, Human Costs, and the Next Chapter For Media and Journalism in an AI-Driven World.

By Paul Gerbino

As a follow up to “The AI Content Gold Rush: Deals, Lawsuits, and the Future of Media,” I thought it would be good to take a deeper dive into the consequences, and possible unforeseen consequences concerning AI and the media.

Following the initial surge of artificial intelligence (A)I licensing deals and legal battles, the landscape of media and AI continues to evolve at a dizzying pace. As we’ve seen, major media companies are navigating a complex web of partnerships and lawsuits, all vying for a stake in the AI-driven future. But beyond the headlines of multi-million dollar deals, several critical aspects demand a closer examination.

While I am not a journalist, or even claim to write well, there are aspects of this new world we find ourselves in that need exploring. The best I can offer is “My Humble Opinion.”

The Ethical Quandaries: Bias, Misinformation, and Displacement

While the pursuit of licensing revenue and technological advancement is understandable, the ethical implications of AI in journalism cannot be ignored. The potential for AI models to perpetuate existing biases, inadvertently spread misinformation, and even fabricate news is a significant concern. Moreover, the specter of journalist displacement looms large. While some argue AI will augment human capabilities, the reality is that certain tasks, particularly those involving data analysis and basic reporting, are increasingly automated.

• Question: How are media companies addressing the ethical concerns surrounding AI-generated content?
• My Humble Opinion:: While many tout “high journalistic standards,” concrete measures are often vague. Independent audits, transparency in AI usage, and rigorous fact-checking protocols are essential but not universally adopted. The issue of bias in training data remains a substantial challenge.

The Human Cost: Impact on Individual Journalists and Editorial Teams

I believe the focus on big AI and media deals often overshadows the impact it has to have on the editorial team, the individual journalists and the freelancers. Their roles have to be transforming, demanding new skills in AI literacy and data analysis. Training programs are emerging, but concerns about job security have to be pervasive.  

• Question: How are editorial teams, individual journalists, and freelancers adapting to the rise of AI?
• My Humble Opinion:: I believe there are two answers to this question. The first answer focuses on high quality journalists, the second focuses on the average to poor journalists. Let me answer the second question first.
  
  Frank Bilotto, one of our readers favorite contributors to the Content Licensing Brief, posted on Instagram “That’s right, the future is here. And let me tell some of you mediocre authors, mediocre journalists, mediocre composers, mediocre screenwriters. Your days are numbered. You better find a new gig. Because ChatGPT can replace you right now.” So that is enough on answer #2.
  
  On answer #1, I believe good to great journalists are upskilling, learning to use AI tools for research and content creation. They are learning, and in some ways, creating ways of using AI to improve what they do.
  
  However, anxieties about automation and the changing nature of journalistic work are widespread. Many fear the loss of nuanced, investigative reporting in favor of AI-generated summaries.   

Public Perception and Trust: Authenticity Under Scrutiny

The public’s trust in news is already fragile, and the introduction of AI raises further questions about authenticity and reliability. Readers are increasingly wary of AI-generated content, fearing manipulation and the erosion of human judgment.  

• Question: How does the public perceive the use of AI in news?
• My Humble Opinion:: I fear the “Good Enough Problem” which I wrote about in my last article “The AI Answer Engine: Friend or Foe to Content Creators?”. The “Good Enough Problem” describes how AI’s convenience and readily available, summarized answers can lead to a decline in critical thinking, curiosity, and the exploration of diverse perspectives, potentially homogenizing information and harming the value of original content.
  
  On another note, surveys that I read suggest a mix of curiosity and skepticism among the public. In any case, transparency is crucial. Clear labeling of AI-generated content, whether to be used for news, education, or any other purpose, are needed to build trust.

The Black Box: Understanding AI Models

The technical details of AI models are often shrouded in mystery. Understanding how these models are trained and how they generate content is essential for informed discourse.

• Question: How do these AI models work, and how are they trained?
• My Humble Opinion:: The reality is that AI has been around for at least 70 years. We called it machine learning, big data, and now Large Language Models (LLMs). Just for fun, here are some of the other terms I found we used:
  • Early AI (1950s-1970s)
    • Thinking Machines 
    • Cybernetics 
    • Expert Systems 
    • Natural Language Processing (NLP) 
    • Artificial Neural Networks 
    • Automata (this one I did not remember at all)
  • Later Developments (1980s-2000s)
    • Cognitive Computing 
    • Intelligent Agents 
    • Data Mining
    • Machine Learning
    • Big Data

Today LLMs are trained on vast datasets of text and code, learning patterns and relationships. They generate text by predicting the next word in a sequence. The quality and bias of the training data directly impact the output. The process is complex, and the inner workings of many of these models remain opaque, which may be part of the problem.  

Democracy at Stake: Information and Public Discourse

The role of a free press in a democratic society is paramount. The potential for AI to amplify misinformation and manipulate public discourse poses a significant threat.  

• Question: What are the long-term implications of AI on democracy?
• My Humble Opinion:: The risk of AI-driven propaganda and the erosion of public trust in reliable information sources is a serious concern. Robust fact-checking mechanisms, media literacy initiatives, and regulatory frameworks are needed to safeguard democratic values.  

The AI Companies’ Perspective: Beyond Profit

This article primarily presents the media companies’ perspective. Understanding the motivations of AI companies is equally important. They see AI as a transformative technology with vast potential for innovation and efficiency.

• Question: What are the motivations of AI companies in partnering with media outlets?
• My Humble Opinion:: Beyond data acquisition, AI companies seek to refine their models, improve accuracy, and build credibility. They also aim to demonstrate the value of their technology in real-world applications.  

Regulation and Policy: Navigating the Legal Maze

The legal landscape surrounding AI in media is still nascent. Clear regulations and policies are needed to address copyright infringement, data privacy, and ethical concerns.  

• Question: What regulations and policies are being developed to govern AI in media?
• My Humble Opinion:: Discussions are ongoing in various jurisdictions. Proposals include copyright protection for training data, transparency requirements for AI-generated content, and ethical guidelines for AI development.

A Global View: Beyond US Borders

The AI revolution in media is a global phenomenon. Examining how other countries are adapting to these changes provides valuable insights.

• Question: How is AI impacting media and journalism in other countries?
• My Humble Opinion:: Globally, AI’s influence on media and journalism is multifaceted. In Asia, notably China and India, there’s a strong push for practical applications like AI-powered news delivery and content creation. However, there are also concerns that these technologies may be utilized for enhanced message tracking and control. In contrast, Europe, including the UK, is primarily focused on establishing ethical guidelines and regulatory frameworks, with a strong emphasis on data privacy and responsible AI deployment. Several European countries are grappling with the dilemma of attracting AI investment while safeguarding their media sectors, leading to anxieties about potential concessions regarding media ownership and the use of content for AI training. Ultimately, the global landscape showcases a diverse range of approaches, each reflecting distinct cultural and regulatory contexts.

Alternative Solutions: Beyond Licensing and Lawsuits

Beyond licensing deals and legal battles, media companies can explore innovative solutions such as collaborative AI development, open-source AI tools, and direct audience engagement platforms.

• Question: What alternative solutions can media companies explore?
• My Humble Opinion:: Consortiums for AI development is an area that may be a solution for small to medium sized publishers. Community-driven fact-checking platforms (though this one makes me nervous on who is training the algorithm), and AI-powered tools for investigative journalism are also promising avenues. Empowering audiences to participate in the verification process can also build trust.

The intersection of media and AI is a complex and evolving landscape. By addressing the ethical, social, and technical dimensions of this transformation, we can ensure that AI serves to enhance, rather than undermine, the vital role of media and journalism in a democratic society.

Editor’s Note:
In Part 1 of this article, we mentioned a report developed by Creative Licensing International titled “Major Media Companies and their AI Deals. If you’d like a copy of the report, send us an email at [email protected]. Thanks.

About Paul Gerbino

Paul Gerbino is the President of Creative Licensing International. He is an expert in digital, content strategy, licensing, product development, advertising, and copyright protections. His expertise is noted with an exemplary track record of transforming low-performing projects into highly profitable revenue streams. Evident in creating and launching innovative digital media products and advertising programs for B2B, B2C, STM, and academic publishers. Paul is passionate about helping publishers improve their performance, productivity, and profitability in the evolving digital landscape.