AI Bias Examples & How to Check AI Content for Biases

October 24th, 2024

AI tools may be effective at improving efficiency and scalability but a major concern with scaling content production with this technology is AI writing bias. Bias in AI-generated content can damage a brand’s reputation, hinder inclusivity, and lead to unintended consequences if not reviewed properly and consistently. In this blog, we’ll explore AI bias examples and how businesses can actively check and address these biases when using generative AI tools.

Defining the Problem of Bias in AI

Bias in AI arises when machine learning models are trained on datasets that reflect human prejudices, incomplete information, or skewed representation. These biases can manifest in AI-generated content as assumptions about race, gender, socio-economic status, or geographical location. While biases in areas such as facial recognition or hiring algorithms have gained significant media attention, bias in AI writing is a growing concern.

In content creation, bias can subtly influence tone, phrasing, or subject matter, perpetuating stereotypes or alienating certain audiences. For example, an AI writing tool might consistently associate male pronouns with leadership roles or reinforce gender stereotypes by suggesting certain occupations based on gender, such as assigning caregiving roles to women and technical roles to men. These patterns can cause reputational damage if not identified and corrected, making it essential for businesses to actively address bias in their AI-generated content.

Real-World AI Bias Examples in Writing

While many companies leverage AI tools to speed up content production, biases embedded in these tools have led to several real-world situations where content became problematic. Here are a couple of these examples:

Gender Bias in Recommendation Letters. AI writing tools of two different LLMs were found to discriminate against women in a study that tested gender bias in recommendation letters. According to researchers, male workers were described with terms associated with “active workers,” while female workers were described with terms associated with “passive objects.” There were distinct differences in the nouns and adjectives used by the writing tools. The output for men included words like “expert” and “respectful” while the output for women included words like “beauty” and “emotional.” These findings highlight how AI-generated content can reinforce traditional gender stereotypes in professional settings.

Age-related Bias & Names. In multiple studies, AI systems have been shown to perpetuate ageism through word-embedding models. Names like “Ruth” or “Horace” were linked to older generations and associated with negative connotations and common aging terms. AI models reinforced more positive language with what were categorized as “young names,” producing a bias against advanced age. These findings underscore the importance of addressing age-related bias in AI writing tools to ensure more balanced and inclusive content.

The Changing Landscape of AI Content Creation

As AI technology continues to revolutionize content production, it’s important to remain vigilant about the biases that can be embedded in machine-generated outputs. The landscape of content marketing has dramatically changed with the integration of AI, improving efficiency and scalability. However, as highlighted in this article on how AI has transformed content marketing, the rapid adoption of AI tools brings its own set of challenges, including the risk of bias.

Businesses need to actively manage these risks by ensuring AI-generated content is reviewed for biases and inaccuracies, maintaining both quality and inclusivity in their communications. As the role of AI expands, balancing innovation with ethical responsibility will be key to leveraging the full potential of these tools.

How to Review AI-Generated Content for Bias

Now that we’ve explored the risks of bias in AI writing, let’s discuss strategies for businesses to ensure their content is free from these biases. Here are actionable steps businesses can take:

Diverse and Inclusive Training Data

One of the primary reasons AI models produce biased content is the data they’re trained on. Ensure that AI models are trained on diverse datasets that represent different genders, ethnicities, socioeconomic backgrounds, and industries. Collaborating with AI providers to confirm the breadth of their datasets is a crucial first step in mitigating bias.

Manual Content Review

While AI tools can generate content quickly, human oversight is essential. After AI has produced content, have a diverse team review it for potential biases, such as inappropriate language, stereotypes, or assumptions. A human editor with a keen understanding of the target audience can help catch subtle biases AI might miss.

Bias Detection Tools

Several tools exist that are specifically designed to detect bias in written content. Tools like Textio and OpenAI’s API offer bias-checking functionalities, allowing businesses to review AI-generated content before publishing. Using these tools on AI-generated content can help catch possible biases and ensure inclusivity in the writing.

Continuous Training and Feedback Loops

AI models aren’t static — they learn and improve based on feedback. Businesses should implement regular training and feedback loops with their AI providers, flagging any biased content and updating the training data accordingly. By consistently refining the model, businesses can reduce the likelihood of biased content being generated in the future.

Contextualizing Content

AI might not always understand the nuance of certain industries, especially in B2B contexts. It’s important to contextualize AI content, especially when communicating complex topics like finance, healthcare, or technology. Ensure that the tone, language, and framing are aligned with industry best practices, and avoid jargon that could perpetuate biases.

Set Clear Guidelines for AI Use

Establish clear guidelines for how AI tools should be used within your organization. This might include ensuring that AI content is used as a draft, not a final product, and that any sensitive or industry-specific content is always reviewed by a subject matter expert before being published.

Additional Steps for Businesses

In addition to the strategies mentioned, businesses should be proactive in educating their teams on the potential for AI bias. This might include training sessions on recognizing and addressing bias in content or creating cross-functional teams to review high-impact content such as B2B proposals, marketing materials, or legal documents.

When you take a collaborative approach between AI tools and human expertise, businesses can then harness the speed and efficiency of AI while ensuring their content remains accurate, inclusive, and representative.

AI Is A Powerful Tool, But Is Bias Undermining Your Strategy?

AI is a powerful tool for content creation, but businesses must address potential biases head-on. Regularly auditing datasets and involving human oversight are essential steps in ensuring AI-generated content remains accurate, inclusive, and aligned with brand values.

As AI continues to evolve, it’s crucial to manage these challenges to protect your brand’s reputation and foster inclusivity in B2B communications. At ContentWriters, we specialize in helping businesses navigate these complexities with our AI content policy and human-centric writing, ensuring your content meets the highest standards of quality and integrity.

Contact ContentWriters today to elevate your content strategy with our industry-specialized, SEO-focused editing services with bias checks and expert reviews.