Conversations about neural networks do not stop. New AI programs and applications are constantly being released. It’s not easy for marketing professionals to keep track of trends, and it’s even harder to choose from a large number of applications that are actually worth using.
Of course, neural networks are not a panacea. They can’t do anything without people. And the most important thing here is ethical issues. Neural networks are practically unable to generate the kind of content that could be immediately, without fear, applied in any industry. There should always be a symbiotic relationship between human activity and technology: AI only sketches out ideas, and humans edit and refine them.
To train a neural network, a huge data set is required. The more data, the more advanced the AI model will be. But access to information can be difficult due to privacy and intellectual property rights issues. There are separate issues of ethics. They will be considered within the framework of this article.
Man or machine?
Today, almost no online business runs without content generated by neural networks.
Technology greatly facilitates the work of specialists: they can enjoy the creative process rather than engage in routine work. However, whenever we use AI, the following questions come to mind.
1. Who owns the content or code generated by the neural network?
If you use AI to write code or blog posts, who owns the copyright? To you? Or a neural network provider? The legislation does not yet have a clear answer to the question of who is the owner of the result of intellectual activity obtained using a neural network.
2. Do you want to help your competitors?
If you create content using a neural network, the data you upload will be used to train other AI models.
We are talking about situations where competitors may be interested in your company’s data, for example, they are curious about closed source code under an NDA or a marketing strategy that is superior to theirs.
3. How to deal with the risk of being sued for using AI-generated content?
There are cases where people have been sued for using content created by neural networks. Even Google has faced similar problems.
4. What if in the future there is no need for manually created content?
Neural networks can generate content that is optimized for search engines. Will this lead to a decrease in the value of “human” content? Or will generated and hand-crafted content complement each other seamlessly?
5. Is it dangerous to transmit confidential and corporate information to neural networks?
AI tools need data to learn and operate. What data is needed? How will they be used? What are the privacy concerns when sharing data with artificial intelligence?
6. Who is responsible for incorrect information?
AI tools and chatbots are not perfect. They make mistakes. What happens when neural networks generate inaccurate content? Who is responsible for this?
AI tools rely heavily on the data they are trained on to produce the final text. Whatever data was at the input will be at the output. Sometimes AI can learn bias and stereotypical thinking.
7. Will AI be able to replace humans in the workplace?
Will neural networks really replace marketers when they can create 10 times more content than humans using only 10% of resources? How will the human decision-making process change? What will happen to people’s rights?
8. How will the introduction of AI affect the cost of content?
If AI can create content that is indistinguishable from human content, will the cost of content go down? People may not want to pay for stuff if they can get it from AI for free.
AI problems become apparent
Generative neural networks are not always accurate. But the main problem is that by loading the AI with “sensitive” information about the company, the AI developer gains access to this information. And he can use it freely.
Therefore, you should not transfer confidential data to neurons without the owner’s permission. When training AI models, it is important to ensure that the training information was obtained legally and does not violate any laws or regulations. Most public generative AI services (such as ChatGPT) do not disclose what datasets they were trained on. As a result, it is unknown who actually owns the data.
What makes the situation worse is that you have no way to check how biased the results are, and AI bias is a real problem. Robots “grab” stereotypes from training data. They can lie very convincingly. Therefore, AI-generated content should not be trusted as the only source of information, especially when it comes to scientific research.
Identifying problems with AI is not difficult. Content generated solely by neural networks is very eye-catching:
- Long, complex paragraphs, repeated pieces of content;
- Awkward, awkwardly shaped cliches and syntactically long sentences;
- Abundant use of adjectives;
- Facts based on subjective opinion.
Ethical Guidelines for the Use of Artificial Intelligence
It is recommended to use generative AI with a license from the company. This is important from a liability and ownership perspective. However, you should not exactly copy the content or code of a neural network. Be sure to edit, change and add to the material to make it unique.
- Data protection is the most important aspect. The accuracy of the information generated by neural networks is questionable and should not be blindly trusted, especially in the case of rapidly changing topics.
- The output of a neural network should not be copied verbatim. Generating content is good, but you should always remember to improve your output to get truly fresh ideas.
- Artificial intelligence is great for surface-level research and testing ideas on a topic. It should not be used to prove your point.
- Most of the information is “sensitive” content. Any information you enter into a neural network may be available to competitors, current and future customers.
- Do not share personal data with AI tools such as ChatGPT. The same applies to customer personal information (names, email addresses, phone numbers and other personally identifiable information).
Using neural networks: pros and cons
You decide for yourself what ethical principles to adhere to and what standards to apply in your company. However, using a neural network to create content from scratch is not the answer.
Find as much information as possible on topics that interest you. Review videos, documents, blogs, drafts, etc. and combine them to create a solid library of information. Then write some of the content yourself, and let the rest be completed by artificial intelligence. Maintain a consistent writing style and add relevant examples and keywords.
Examples of misuse of neural network tools:
- Creating content for marketing emails using personalized licenses
- Entering company financial data into the AI algorithm;
- Create code using neural networks and use it without making changes.
Examples of effective use of AI tools:
- Using corporate licenses to create content for email newsletters; testing and careful editing of content before publication;
- asking neural networks to improve algorithms without handing over the code to AI;
- Come up with ideas for new marketing campaigns and analyze how they can benefit the business.
Everyone knows that your content is written by a neural network
Let’s leave aside ethical issues related to AI, privacy and cybersecurity. One of the biggest problems that arises when AI is used incorrectly is that text written by neural networks is quite easy to recognize.
Why is this a problem?
Let’s say you use AI to automate all your cold emails. Microsoft’s Outlook and Google’s Gmail are the two largest email providers. To protect their clients from spam , they mark emails generated by neural networks as spam. The ability to detect these messages has improved, and even if the message you create looks impressive, it will be useless when it doesn’t make it into the prospect’s inbox.
It may seem like it’s just an email. However, AI-based phishing attacks are becoming more common. Major technology companies, wanting to protect their customers from such attacks, are blocking content entirely generated by neural networks.
People will not be interested in your letter if they suspect that all its contents were written by a neural network. Increasing the volume of text at the expense of its quality will most likely cost you and the company as a whole.
However, the use of AI and machine learning algorithms is not a crime. Not all content needs to be generated by artificial intelligence. It’s feasible to use AI for inspiration, planning and headline creation, but it shouldn’t completely replace human work.