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 generative AI that uses machine learning and large language models (LLMs) to create easy-to-use, natural language interfaces.

Businesses and individuals have started to incorporate AI technologies into their daily lives. According to a survey from McKinsey, 22% of people surveyed now use AI in some form for their work.

Although generative AI has many benefits, there are some risks to learn before using it. This guide will explore the risks of AI content, starting with 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 image generation
• Website 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 data sets 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.

AI content risks and workarounds

As you can see, advanced AI offers many benefits for content generators, and it has its drawbacks as well. 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 they used AI content without proper quality control, leading to substantial errors in their 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 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 and not users, you’ll 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 loss of customer trust, especially 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 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 content 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.

Low-quality content

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

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

Cybersecurity concerns

As AI development in content creation advances, new cybersecurity concerns emerge as well. AI-generated content 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 towards 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 AI is improving

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

• 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. One problem with many current language models is that you run them in the cloud. OpenAI, Anthropic, and Google all offer great products—but you must trust them with your data. Doing this is challenging for companies in regulated industries. But new 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 creating reliable outputs—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, there are many advancements coming to AI-generated content, so keep updated with the newest tools.

Put your content creation skills to work

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.

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Disclosure: Upwork is an OpenAI partner, giving OpenAI customers and other businesses direct access to trusted expert independent professionals experienced in working with OpenAI technologies.

Upwork does not control, operate, or sponsor the other tools or services discussed in this article, which are only provided as potential options. Each reader and company should take the time to adequately analyze and determine the tools or services that would best fit their specific needs and situation.