AI-generated content has exploded over the last year, boosted by the notorious ChatGPT, copywriting-focused SaaS tools, generative AI products that can create images and videos, and multiple CMS platforms launching new AI features.

Among the hype, one of the biggest questions marketers and companies using AI need to consider are the ethical ones surrounding authorship, ownership, and transparency.

In this blog, we’ll cover what constitutes AI-generated content, the critical ethical considerations to be made, the biases in AI-generated materials, and outline strategies for overcoming the challenges.

What is AI Generated Content?

AI-generated content is created by artificial intelligence and machine learning algorithms rather than humans. AI-generated content requires humans to input written text prompts into a generative AI solution, resulting in blogs, images, and various other content assets. Here are some examples:

Generative AI Tool: ChatGPT 3.5

Prompt: Write an introduction to a blog that talks about the ethical considerations of generative AI. Keep the output to one paragraph.

Result: Welcome to a thought-provoking exploration of the fascinating realm of generative AI and the ethical considerations that accompany its rapid advancements. As we marvel at the incredible capabilities of machines to generate human-like text, images, and even music, it becomes imperative to delve into the ethical implications of this cutting-edge technology. In this blog, we will navigate the complex landscape of generative AI, examining the ethical challenges it poses in terms of privacy, bias, and the potential for misuse. As we stand at the intersection of innovation and responsibility, join us in a critical examination of the ethical considerations surrounding generative AI, seeking a nuanced understanding of the impact it may have on society, creativity, and the very fabric of human-machine collaboration.

Generative AI Tool: DALL-E2

Prompt: A photo of a teddy bear on a skateboard in Times Square

Result:

Mapping the Growth of AI Content

According to Bloomberg, the generative AI market is set to grow to $1.3 trillion over the next 10 years from a market size of $40 billion in 2022. But what’s causing this exponential growth?

Technology Advancement

The primary reason for the growth of AI content is the advancement of technology that makes it possible in the first place. Large language models (LLMs), the deep learning models used to train AI, have improved over the years.

As machine learning algorithms and natural language processing (NLP) continue to improve and get faster and more efficient, creating content using generative AI will become easier.

Multiple Use Cases

In previous examples, we highlighted how generative AI could be applied to create written content like blogs and images. However, that only scratches the surface. Artificial intelligence can also create long-form novels, video game storylines, software code, and personalized content.

Productivity & Convenience

Generative AI enables humans to work faster and more efficiently. With the increase in productivity and convenience it offers employees, the continued growth of AI-generated content is to be expected.

Ethical Concerns for AI-Generated Content

When using AI-generated content, there are various ethical concerns that anyone using the content should take into account.

Sharing of Harmful Content

Discriminatory content or content that promotes violence or misinformation can be harmful to your audience. Without careful monitoring, there is a risk that this type of information could be disseminated by generative AI and negatively impact ostracized individuals or communities. For example, if AI is used to create a light-hearted or witty company-wide email, but that email contains offensive language, it could create disharmony within the organization.

Embedded Bias and Discrimination

Generative AI tools are only as good as the data used to train the algorithms. Unfortunately, AI models may inadvertently amplify biases present in the data they were trained on, which can neglect or be discriminatory towards diverse groups or reinforce societal biases that aren’t necessarily true.

Inaccuracy

One of the dangers of AI-created content is inaccuracy. When sharing information generated by AI, it’s critical to double-check for inaccuracies as it can lead to the spread of misinformation, which influences public opinion about a business or affects the decisions made.

Plagiarism

Unintentional or deliberate plagiarism is a significant ethical concern with AI-generated content. For enterprises, there can be legal and brand loyalty consequences if content is plagiarized. Artificial intelligence is meant to augment, not replace, humans. If not properly guided and supervised, AI systems may inadvertently reproduce existing content without proper attribution, undermining the principles of intellectual property and fair use.

Privacy and Data Protection

Companies today need to ensure that they have proper user data handling and consent management guidelines. If personal customer information is used to create AI content, it can be an ethical problem, particularly concerning data privacy regulations and safeguarding privacy rights.

Sensitive Information Disclosure

AI-generated content might inadvertently reveal sensitive information, posing ethical concerns related to privacy and confidentiality. Organizations must implement safeguards to prevent the unintentional disclosure of confidential or sensitive data through AI-generated content.

Copyright and Legal Exposure

Who owns AI-generated content? The company or individual using it or the company that owns the generative AI tool that created it? Ethical concerns arise when there is ambiguity regarding the ownership of AI-generated material and the potential legal exposure for individuals or organizations that publish it without proper authorization.

Best Practices for Ethical AI Content Creation

In order to create content that maintains the highest ethical standards, it’s essential to follow best practices.

1. Define a purpose for the content

The first and most crucial step when creating AI content is to define a purpose for the content. This helps mitigate the risk of generating harmful or inappropriate material by aligning content creation with organizational goals.

2. Input clear instructions with guardrails and constraints

Generative AI tools can only produce results as good as the given prompts. Providing explicit instructions to AI models, along with well-defined guardrails and constraints, helps prevent the generation of biased or discriminatory content.

3. Follow global guidelines and standards

As generative AI has grown, so has the fear that humans could lose control over artificial intelligence, and it will be hard to distinguish between what was created by a human and what was created by a robot.

This has prompted many governments and companies to create guidelines and policies about using AI-created content. Following these guidelines, whether global or within the confines of an organization, is essential to maintaining ethical standards.

4. Use diverse data input methods and sources

Avoiding the issue of bias and discrimination with AI models remains difficult. However, models can learn from a wide range of perspectives. Ensuring that input methods and sources for training algorithms are as diverse as possible reduces the risk of reinforcing existing biases creeping into the output from the AI.

5. Monitor and evaluate output

Regularly monitoring and evaluating the output of AI-generated content is critical for ensuring accuracy and identifying potential ethical issues. Continuous oversight allows organizations to promptly detect and address unintended consequences and continue training algorithms for better outputs.

6. Fact-check with subject matter experts

Generative AI tools have been known to produce inaccurate information. This ranges from not fully understanding initial prompts to pulling statistics and arguments out of thin air with no sources to back them up. As such, consulting subject matter experts adds a layer of human expertise to AI-generated content and helps verify the accuracy and relevance of the generated material.

7. Incorporate quality control processes

Along with using subject matter experts, companies should incorporate their own quality control processes. This could include multiple review stages to double or triple-check for accuracy, originality, and adherence to ethical standards. Doing so helps prevent plagiarism and ensures the overall integrity of created content.

Optimize Your AI-Content Strategy

Artificial intelligence will continue to expand and evolve in the coming years. Although there are documented productivity and efficiency improvements from using AI, content creators must also consider the ethical implications.

Content Bloom stays ahead of the curve when it comes to digital trends and can help enterprises deliver content solutions that impact the bottom line without causing ethical harm. Using our digital marketing expertise, we can assist in building a marketing strategy that resonates with your audience.

Additionally, we provide guidance on the right tools to help execute that strategy, including CMSs and DXPs with added AI capabilities to help streamline content creation and the workflows to help you maintain ethical standards and accuracy.

Let’s discuss how Content Bloom can help you with how AI can benefit your organization and the potential ethical implications.