33 Problems With Media in One Chart
One of the hallmarks of democratic society is a healthy, free-flowing media ecosystem.
In times past, that media ecosystem would include various mass media outlets, from newspapers to cable TV networks. Today, the internet and social media platforms have greatly expanded the scope and reach of communication within society.
Of course, journalism plays a key role within that ecosystem. High quality journalism and the unprecedented transparency of social media keeps power structures in check—and sometimes, these forces can drive genuine societal change. Reporters bring us news from the front lines of conflict, and uncover hard truths through investigative journalism.
That said, these positive impacts are sometimes overshadowed by harmful practices and negative externalities occurring in the media ecosystem.
The graphic above is an attempt to catalog problems within the media ecosystem as a basis for discussion. Many of the problems are easy to understand once they’re identified. However, in some cases, there is an interplay between these issues that is worth digging into. Below are a few of those instances.
Editor’s note: For a full list of sources, please go to the end of this article. If we missed a problem, let us know!
Explicit Bias vs. Implicit Bias
Broadly speaking, bias in media breaks down into two types: explicit and implicit.
Publishers with explicit biases will overtly dictate the types of stories that are covered in their publications and control the framing of those stories. They usually have a political or ideological leaning, and these outlets will use narrative fallacies or false balance in an effort to push their own agenda.
Unintentional filtering or skewing of information is referred to as implicit bias, and this can manifest in a few different ways. For example, a publication may turn a blind eye to a topic or issue because it would paint an advertiser in a bad light. These are called no fly zones, and given the financial struggles of the news industry, these no fly zones are becoming increasingly treacherous territory.
Misinformation vs. Disinformation
Both of these terms imply that information being shared is not factually sound. The key difference is that misinformation is unintentional, and disinformation is deliberately created to deceive people.
Fake news stories, and concepts like deepfakes, fall into the latter category. We broke down the entire spectrum of fake news and how to spot it, in a previous infographic.
Mass media and social feeds are the ultimate Darwinistic scenario for ideas.
Through social media, stories are shared widely by many participants, and the most compelling framing usually wins out. More often than not, it’s the pithy, provocative posts that spread the furthest. This process strips context away from an idea, potentially warping its meaning.
Video clips shared on social platforms are a prime example of context stripping in action. An (often shocking) event occurs, and it generates a massive amount of discussion despite the complete lack of context.
This unintentionally encourages viewers to stereotype the persons in the video and bring our own preconceived ideas to the table to help fill in the gaps.
Members of the media are also looking for punchy story angles to capture attention and prove the point they’re making in an article. This can lead to cherrypicking facts and ideas. Cherrypicking is especially problematic because the facts are often correct, so they make sense at face value, however, they lack important context.
Simplified models of the world make for compelling narratives, like good-vs-evil, but situations are often far more complex than what meets the eye.
The News Media Squeeze
It’s no secret that journalism is facing lean times. Newsrooms are operating with much smaller teams and budgets, and one result is ‘churnalism’. This term refers to the practice of publishing articles directly from wire services and public relations releases.
Churnalism not only replaces more rigorous forms of reporting—but also acts as an avenue for advertising and propaganda that is harder to distinguish from the news.
The increased sense of urgency to drive revenue is causing other problems as well. High-quality content is increasingly being hidden behind paywalls.
The end result is a two-tiered system, with subscribers receiving thoughtful, high-quality news, and everyone else accessing shallow or sensationalized content. That everyone else isn’t just people with lower incomes, it also largely includes younger people. The average age of today’s paid news subscriber is 50 years old, raising questions about the future of the subscription business model.
For outlets that rely on advertising, desperate times have called for desperate measures. User experience has taken a backseat to ad impressions, with ad clutter (e.g. auto-play videos, pop-ups, and prompts) interrupting content at every turn. Meanwhile, in the background, third-party trackers are still watching your every digital move, despite all the privacy opt-in prompts.
How Can We Fix the Problems with Media?
With great influence comes great responsibility. There is no easy fix to the issues that plague news and social media. But the first step is identifying these issues, and talking about them.
The more media literate we collectively become, the better equipped we will be to reform these broken systems, and push for accuracy and transparency in the communication channels that bind society together.
Infographic: Generative AI Explained by AI
What exactly is generative AI and how does it work? This infographic, created using generative AI tools such as Midjourney and ChatGPT, explains it all.
Generative AI Explained by AI
After years of research, it appears that artificial intelligence (AI) is reaching a sort of tipping point, capturing the imaginations of everyone from students saving time on their essay writing to leaders at the world’s largest tech companies. Excitement is building around the possibilities that AI tools unlock, but what exactly these tools are capable of and how they work is still not widely understood.
We could write about this in detail, but given how advanced tools like ChatGPT have become, it only seems right to see what generative AI has to say about itself.
Everything in the infographic above – from illustrations and icons to the text descriptions—was created using generative AI tools such as Midjourney. Everything that follows in this article was generated using ChatGPT based on specific prompts.
Without further ado, generative AI as explained by generative AI.
Generative AI: An Introduction
Generative AI refers to a category of artificial intelligence (AI) algorithms that generate new outputs based on the data they have been trained on. Unlike traditional AI systems that are designed to recognize patterns and make predictions, generative AI creates new content in the form of images, text, audio, and more.
Generative AI uses a type of deep learning called generative adversarial networks (GANs) to create new content. A GAN consists of two neural networks: a generator that creates new data and a discriminator that evaluates the data. The generator and discriminator work together, with the generator improving its outputs based on the feedback it receives from the discriminator until it generates content that is indistinguishable from real data.
Generative AI has a wide range of applications, including:
- Images: Generative AI can create new images based on existing ones, such as creating a new portrait based on a person’s face or a new landscape based on existing scenery
- Text: Generative AI can be used to write news articles, poetry, and even scripts. It can also be used to translate text from one language to another
- Audio: Generative AI can generate new music tracks, sound effects, and even voice acting
People have concerns that generative AI and automation will lead to job displacement and unemployment, as machines become capable of performing tasks that were previously done by humans. They worry that the increasing use of AI will lead to a shrinking job market, particularly in industries such as manufacturing, customer service, and data entry.
Generative AI has the potential to disrupt several industries, including:
- Advertising: Generative AI can create new advertisements based on existing ones, making it easier for companies to reach new audiences
- Art and Design: Generative AI can help artists and designers create new works by generating new ideas and concepts
- Entertainment: Generative AI can create new video games, movies, and TV shows, making it easier for content creators to reach new audiences
Overall, while there are valid concerns about the impact of AI on the job market, there are also many potential benefits that could positively impact workers and the economy.
In the short term, generative AI tools can have positive impacts on the job market as well. For example, AI can automate repetitive and time-consuming tasks, and help humans make faster and more informed decisions by processing and analyzing large amounts of data. AI tools can free up time for humans to focus on more creative and value-adding work.
How This Article Was Created
This article was created using a language model AI trained by OpenAI. The AI was trained on a large dataset of text and was able to generate a new article based on the prompt given. In simple terms, the AI was fed information about what to write about and then generated the article based on that information.
In conclusion, generative AI is a powerful tool that has the potential to revolutionize several industries. With its ability to create new content based on existing data, generative AI has the potential to change the way we create and consume content in the future.
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