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Some Advice For Journalists Writing About AI

Some Advice For Journalists Writing About AI

Before ChatGPT came onto the scene, major news organizations had been using AI for years. But many journalists are still learning how to write about AI in ways that are accurate and unbiased.

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ML-augmented content production processes can be used at every stage of the journalistic value chain. This includes information gathering, fact checking and writing.

How to report on AI accurately

One of the major concerns about AI in news reporting is that it can lead to inaccurate, biased, and unreliable content. This is because AI programs can only be as accurate and trustworthy as the data that they are trained on. Moreover, the accuracy of AI-generated news articles can be undermined by a lack of transparency about how and where that content was produced.

As such, journalists should take care when reporting on AI and be sure to clearly distinguish between the different types of AI-based software systems that are available. For example, it is important to not confuse AI that enables robots to drive cars with the type of general intelligence that can enable them to play Space Invaders and Montezuma’s Revenge.

Nonetheless, it is clear that advances in AI can support high-quality journalism at every stage of the journalistic value chain. This includes gathering and verifying information; identifying suitable informants; composing content; and presenting and disseminating the news to the public. Moreover, AI can also help make journalists more efficient by automating mundane tasks and freeing them up for more important work. However, this requires a careful balance between the benefits of efficiency and the need to ensure that the information produced by AI is accurate and trustworthy.

What are common AI misconceptions

One of the most common misconceptions about AI is that it works in a way similar to human minds. This is an inaccurate and dangerous assumption, and it can lead to incorrect interpretations of unexpected system behavior. For example, if a Tesla car suddenly stops for no reason, the driver may attribute it to a conscious decision of “the AI,” rather than realizing that it’s a not-yet understood aspect of a complex technical system.

Another common misconception is that AI is inherently neutral or trustworthy. While it’s true that some AI algorithms are more reliable than others, it’s important to remember that AI is still a new technology. As such, it can be prone to bias and other problems that can compromise its reliability.

It’s also important to distinguish between AI and machine learning (ML). Often, the terms are used interchangeably, but they have distinct functions. For example, if a computer can recognize bagels and donuts in a photograph, this is considered AI, but if it can understand the context of those bagsels and donuts in order to reach that same conclusion, it’s ML. This distinction is crucial, especially when writing about AI to avoid confusion among readers.

What are ethical concerns around AI

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As AI evolves, it is challenging frameworks for ethics and human rights. It is becoming a hot topic of societal and scientific debates, with new ethical concerns centering around privacy, bias and discrimination, safety and security, economic distribution, democracy and warfare.

While there are many positive aspects of incorporating AI into journalism, there are also some real concerns. One of the biggest is that AI can cause mistakes, which could lead to misinformation and misunderstandings. This is why it’s important for journalists to be aware of what the limitations are of their current technology and be careful not to put too much faith in AI when reporting on complex issues.

Another concern is that AI can become biased and make decisions based on its own prejudices. This is a problem that all industries need to be vigilant about, but it’s especially important in the world of journalism. For example, if an AI system is making split-second decisions about firing a missile or sending in an airstrike, there needs to be a human who can take control in the event of a crisis.

One way that newsrooms can mitigate these risks is by creating clear guidelines on how they will use AI and what their boundaries are. For example, Wired has a page that outlines how they plan to use AI (to suggest headlines or potential cuts to shorten a story) and what they will not do (like using AI-generated images).

How to explain AI jargon simply

Artificial intelligence can be complicated and confusing. With reports about the latest AI developments seemingly popping up on a daily basis, it can be difficult for working professionals to keep up with the latest trends and developments in the field. This is especially true when it comes to machine learning, which is one of the most important aspects of AI that everyone should have a basic understanding of.

Essentially, machine learning is the process of using computers to learn from data. It uses algorithms to identify patterns and relationships in data and use that information to make decisions. This is different than traditional software, which is often programmed to do a single task and can’t adapt to new situations.

There are many different types of machine learning algorithms, which can be used for a variety of purposes. Some of the most common include generative, deep, and large language models. Generative AI is a type of machine learning that uses learning algorithms to create new digital images, video, audio, and text. This is different than extractive AI, which is designed to recognize patterns and extract pre-existing data. Deep AI is a type of machine learning that takes advantage of computational power to train on larger datasets and more complex tasks. This is sometimes referred to as “advanced” or “strong” AI.

Who are important AI experts

With chatbots and robots already making their way into customer service, the rise of generative AI writing tools like Quill and a host of new innovations in the field, it’s no secret that artificial intelligence is having an ever-increasing impact on our lives. With tech giants investing billions in new AI products and services, universities integrating more AI into their curricula and even the Department of Defense stepping up its AI game, we’re living in a time when the future is AI.

Computer programmers are key players in the AI landscape, as they are the ones who decide what goes into an algorithm and how it will make decisions. This means they have the power to make AI algorithms that are fair and equitable, or ones that discriminate against people. A huge challenge for these programmers is figuring out how to avoid bias in their programming and data, which is a problem all humans face all the time.

Another big name in the world of AI is Kate Nahrstedt, who has dedicated her career to studying the social and ethical implications of the technology. Her research has been featured in a range of publications, including Harper’s Bazaar and Sage Journals. She also works as a consultant for tech startups and is active on Twitter, where she frequently discusses the impact of AI and how it can be used responsibly.

What jobs will AI replace

While the use of AI is transforming all industries, not every job is in danger of being eliminated. Blue-collar jobs like manufacturing will be replaced by automation, but white-collar jobs that require critical thinking and creativity remain safe. However, jobs in the middle—including customer service, data entry, and sales—will likely disappear as companies automate tasks that can be performed by a machine.

AI is already replacing some jobs, including those in sales and marketing. For example, a machine can analyze a sales call and identify patterns in behavior much faster than a human could. In addition, AI is increasingly being used to develop e-commerce strategies and content for marketers.

In journalism, AI is being used to write articles and transcribe interviews. A notable example is Yle’s robot journalism, which uses data to create schematic articles on topics such as ice hockey results. However, it remains important for journalists to be able to make informed decisions about which stories are appropriate for automation and to ensure that they follow journalistic ethics.

Although AI is helping to improve the quality of writing in many ways, it cannot replace writers completely. This is because creative and original writing still requires a level of thought and creativity that AI cannot replicate. For this reason, it will be impossible for AI to replace writers for the foreseeable future.

How to regulate AI technology

While AI tools are starting to seep into newsrooms, there’s a lot of concern about what this means for journalism. For example, if a machine generates a well-written article that is almost indistinguishable from human writing, could it be considered plagiarism or even fabulism?

Ultimately, the answer lies in transparency and accountability. Newsrooms need to clearly communicate how and why they’re using AI so that their audiences can understand the reasoning behind these decisions. This will help to avoid confusion and misunderstandings down the line.

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Additionally, newsrooms need to ensure that they’re regulating the use of these tools properly and in accordance with ethical standards. This includes ensuring that they’re not violating any laws or regulations regarding privacy or bias.

One way to do this is by introducing a clear policy on the use of AI, like Wired has done. This will help to establish trust with readers and make it easier for them to find out whether or not a story is sourced by AI. It’s also important to remember that AI is a tool that should be used as an aid for journalists, not as a replacement for them. For now, the best thing you can do is to get familiar with these tools and start testing them out.