The Risks of AI-Generated Content and How To Address Them

Content generated by artificial intelligence (AI) is being posted online with increasing frequency. Creators use generative AI for many tasks, including marketing content, online research, scientific applications, and more.

One of the biggest contributors to this has been the generative pretrained transformer (GPT) from OpenAI,  ChatGPT. ChatGPT is part of a family of gen AI that uses machine learning and large language models (LLMs) to create easy-to-use, natural language processing and interfaces.

Businesses and individuals have started to incorporate AI technologies into their daily lives. According to a survey from McKinsey, 78% of people surveyed say their organizations now use AI in some form.

Although generative AI has many benefits, there are some risks to understand before using it. This guide explores the risks of using AI content, starting with explaining what AI content is.

AI in content creation: an overview

Artificial intelligence in content creation leverages machine learning algorithms to analyze vast datasets and generate content based on user input. At the core of this process are sophisticated AI models, particularly deep learning systems and advanced language models like GPT-3 and GPT-4. These models use complex algorithms to process and understand patterns in data.

The process begins with gathering and cleaning training data, a crucial step that directly impacts the quality and capabilities of the resulting AI model. This training data is then used to develop and refine the AI models through iterative learning processes.

Larger language models, such as GPT-3 and GPT-4, are trained on enormous datasets, allowing them to generate diverse and contextually relevant content. Smaller models offer the advantage of fine-tuning on specific datasets, allowing for more tailored content generation.

This fine-tuning process involves additional training on domain-specific data, enabling the AI to produce more specialized and accurate outputs for particular use cases.

Common use cases

AI capabilities for content creation span various formats and purposes. Some common applications for marketing content creation include:

• Blog content generation (ideas, outlines, drafts)
• Social media post creation
• Product description writing
• Graphic design through AI image generation
• Web page copy development
• Brainstorming automation
• First draft development
• Concept generation for existing content

Limitations

While AI content generation offers numerous benefits, it's important to note its limitations:

• Inconsistent quality. The output can vary in quality from one generation to the next, often due to differences in the datasets used for training.
• Better for shorter content. AI typically performs better when creating shorter pieces (a few hundred words or less).
• Multiple versions needed. To ensure optimal results, content creators often need to request multiple versions of a draft and perform validation on each.
• Requires human oversight. All AI-generated content, whether text or images, should be thoroughly checked for quality, accuracy, and appropriate tone before use.
• Lack of contextual understanding. AI may miss nuances or contextual details that a human writer would naturally incorporate.
• Potential for bias. AI can inadvertently reflect biases present in its training data.
• Limited creativity. While AI can combine existing ideas, it may struggle with truly novel or creative concepts requiring human intelligence.
• Hallucinations. At times, AI can actually create content that is untrue.

AI content risks and workarounds

As you can see, advanced AI offers many benefits for content generators in a variety of uses. However, there are also potential risks and vulnerabilities to consider when using AI for content creation and other business applications. We outline them below, along with safeguards for mitigating each.

Misinformation

AI can output misinformation (inaccurate content spread without malicious intent) and worse, disinformation (false information deliberately created and disseminated to mislead or manipulate others). This can lead to bad actors creating deepfakes: AI-generated media, typically videos or audio, that realistically depict people saying or doing things they never actually said or did.

This is due to the AI alignment problem, the challenge of ensuring that artificial intelligence systems behave in ways that are aligned with human values, intentions, and goals. CNET faced this issue when it used AI content without proper quality control, leading to substantial errors in its pieces that needed correction.

To prevent misinformation in AI content, implement rigorous fact-checking, especially when outputs are to be used in a major decision-making process. Before using AI-generated content, fact-check every statement for accuracy and quality. Look for any biases that may have slipped into the material.

SEO problems

Overreliance on AI-powered search engine optimization (SEO) can lead to generic, robotic content that doesn't match your brand's voice or meet user search intent. If you use AI to rank for Google bots rather than users, you’ll actually risk poor SEO performance. Google checks for this with ranking algorithms, so don’t overuse AI with SEO.

Prioritize human decision-making in the content creation process. Have people verify the accuracy and quality of AI-produced content, ensuring it matches your brand's voice and meets user search intent. Use AI as an assistant rather than fully automating your content creation process.

Lack of transparency

Using AI systems without transparency can lead to a loss of customer trust. This is especially true when it comes to data privacy and the use of AI in health care, a field with sensitive regulatory requirements such as HIPAA.

Be transparent about AI using people's personal data, particularly in customer-facing applications like AI chatbots. Inform customers when AI is being used and provide options for human interaction when necessary. Another AI safety best practice is to tell stakeholders and users about your data privacy and risk management policies, including those regarding cybersecurity.

Copyright infringement

Another ethical consideration of using AI-generated text is that AI models may use copyrighted material in their training data without permission. Consider if the AI output references copyrighted material and evaluate usage rights before publishing.

Plagiarism

AI tools don’t copy content verbatim, but they do generate text based on patterns from training data, which can sometimes lead to outputs that closely resemble existing sources. This raises concerns about unintentional plagiarism or copyright infringement, especially if the AI was trained on content without proper rights or attribution. Before publishing, it's important to check AI-generated text for duplication and originality.

Use plagiarism detection tools like Copyscape or Grammarly, and make sure your final content is transformed, sourced, and fully owned by your business or client.

Low-quality content

The ease of producing content with AI could lead to an oversaturation of low-quality content.

To stand out amid this, use AI as an assistant to enhance human creativity rather than replace it. Integrate generative AI tools into your workflows to streamline repetitive tasks and brainstorm ideas with human actors maintaining a focus on high-value creative work.

Cybersecurity concerns

As AI development in content creation advances, new cybersecurity concerns emerge as well. AI writing poses unique risks that policymakers and content creators must address through targeted initiatives.

One significant risk is the potential for cyberattacks leveraging AI-generated content. Bad actors could use advanced AI to create convincing phishing emails, fake websites, or other deceptive content that bypasses traditional security measures. This AI-powered social engineering could lead to data breaches, financial fraud, or other security incidents.

Moreover, the systems used to generate AI content could themselves become targets. If compromised, these AI models could be manipulated to produce malicious content at scale, potentially causing widespread disinformation campaigns or reputational damage.

To mitigate these risks:

• Implement robust verification processes for AI-generated content, especially for sensitive or high-stakes communications.
• Regularly update and secure AI content generation systems to protect against vulnerabilities.
• Train employees to recognize potential AI-generated security threats in content.

As we move toward more sophisticated AI systems, including potential artificial general intelligence (AGI), the risks associated with AI-generated content could escalate. While not an immediate existential risk, the long-term implications of highly advanced AI content generation capabilities must be considered in cybersecurity strategies.

How to vet AI-generated content before publishing

Even with the best tools, AI-generated text still needs human oversight before publishing. Whether you're using content for blog posts, product descriptions, or social media, you'll want to ensure it meets editorial, legal, and SEO standards. Here are some best practices for reviewing AI-generated content:

• Run a plagiarism check. Use tools like Grammarly or Copyscape to check for duplicate content across the web. This helps protect your intellectual property and avoid ranking issues.
• Fact-check claims. Complete due diligence, especially for data, legal, medical, or financial content. Large language models (LLMs) can sound confident while delivering incorrect information.
• Add citations when needed. If AI references outside sources or statistics, verify them and add proper attribution.
• Assess tone and accuracy. Review for voice consistency, especially in branded content. Adjust anything that sounds robotic or lacks a human touch.
• Optimize for SEO. AI can support keyword integration, but be sure the result still reads naturally. Use tools like Clearscope or Surfer SEO to guide your revisions.

When to bring in an expert for review

Even with safeguards in place, it often makes sense to bring in an extra set of eyes. Hiring a freelance editor or compliance expert can help validate your AI-generated content, particularly in sensitive areas like health, finance, or legal content. On platforms like Upwork, you can find professionals who specialize in reviewing content for SEO alignment, bias, tone, or regulatory compliance.

How AI is improving

Generative AI can offer a ton of value today, but its technological advancements aren't finished yet. Many trends in AI technology worth paying attention to are advancing:

• Fine-tuned models. One of the problems with generative models today is the generic tone. You must put a lot of effort into your prompts to have the AI output content in the style you want. With fine-tuned prompts, you can make this happen. You fine-tune prompts by providing a secondary set of training data to an AI, letting it train on your data and style.
• Private language models. A problem with many current language models is that you run them in the cloud. OpenAI, Anthropic, and Google all offer high-quality products, but you must trust them with your data. Doing this is challenging for companies in regulated industries. But now, private language models allow you to run generative AI locally. New models from Meta and Stable Diffusion run locally and allow you to train your private data for fine-tuning.
• New content formats. Generative AI has started to see widespread use in image and text generation. Other forms haven’t been as reliable for generating content, but that’s starting to change. Audio and video content generation is improving, so expect more AI-generated content in those formats.

As you can see, advancements are coming to AI-generated content, so keep updated with the newest tools.

New formats in AI content creation

While most AI discussions focus on writing tools like ChatGPT or Microsoft Copilot, creators are also using generative AI to produce images (e.g., DALL·E), voiceovers, podcasts, and even short videos. These newer formats are gaining traction in content marketing and offer additional ways to scale creative production.

Find or offer AI-powered content creation services

Are you a content creator looking for opportunities to put your skills to use? Browse for content creation jobs on Upwork to find your next customers.

If you're a business that sees the value in using AI for content but would like to avoid unintended consequences, browse the Upwork Talent Marketplace to find experienced AI content creators who can help.