How Is AI Biased?

Key takeaways

• AI Algorithms aren’t neutral and can perpetuate or even magnify the same prejudices and inequalities present in the human-created data they are trained on.
• In content creation, AI bias occurs when models use unbalanced or incomplete training data, leading to a lack of diverse global and cultural viewpoints.
• Examples of content bias include promoting Eurocentric beauty standards, reinforcing the dominance of majority cultures, or disproportionately targeting younger demographics.

We live in a world where algorithms capture, learn, and predict what we’ll like, and they no doubt guide our choices, our interactions, and even our perceptions.

But there’s a problem with algorithms — a problem that predates artificial intelligence (AI) itself: Bias. 

This blog examines where AI bias comes from, how it affects us, and what we can do about it. Before we can communicate inclusively through AI-generated content, we need to confront some uncomfortable truths about the present.

How human bias became algorithmic bias

The truth is, AI isn’t neutral. It mirrors us, with all our imperfections, and sometimes magnifies them in ways we never expected. Human bias becomes algorithmic bias if we’re not careful. Despite AI systems’ attempts at objectivity, these tools perpetuate the same prejudices and inequalities we hoped they could overcome. 

AI bias in content creation

AI bias shows up in content creation when models use biased training data. For instance, a language model trained mostly on English content from Western countries might focus on Western cultural perspectives and miss out on diverse global viewpoints.

Other ways this might show up include:

• An AI system that struggles to accurately represent regional dialects or minority languages, reinforcing the dominance of majority cultures in content output.
• A generative AI that disproportionately targets younger users when creating advertisements for tech products. It may overlook older demographics who are equally interested in technology.
• AI models generating marketing content that reflects stereotypical or outdated ideas. For example, when creating a product description for beauty products, the model might default to language that emphasizes Eurocentric beauty standards, potentially alienating people with different ethnicities.

Subtle biases in written content limit inclusivity and effectiveness, causing brands to miss out on engaging a broader audience. To make AI more ethical, we need to learn how to identify examples of bias and understand how it introduces these tendencies during the data collection process for Large Language Models (LLMs).

Examples of AI bias in algorithms

Giving implicit biases a name helps us catch the specific cognitive shortcuts or prejudices that affect AI models’ judgment. The use of common terminology enables more deliberate efforts to prevent dubious outputs, resulting in more balanced, inclusive, and objective outcomes, whether in writing, hiring, or decision-making.

For example, in an enterprise content creation scenario, understanding “stereotype threat” or “framing bias” allows teams to collaboratively identify and address how content might be unintentionally perpetuating harmful narratives. 

This table walks you through some of the most common types of bias in AI:

How to avoid bias when creating AI-generated content

It’s hard to know what you don’t know, but we all agree that generative models have limitations. Avoiding bias when creating AI-generated content is then a matter of intentionally learning to recognize typical cases when you review content. 

Here are some tips to put you in the right frame of mind for picking up on these warning signs:

1. Understand the data you’re working with

First, get to know the training data used in the model. Biases often come from unbalanced or skewed datasets. If the AI algorithms mostly learn from data from a specific demographic, culture, or language, they’ll likely show those biases in their outputs. If you see this happening, add more diverse and inclusive content to balance out the LLM’s output. Keep checking and monitoring the algorithm or model regularly in your business.

2. Use AI as a collaborative tool

Secondly, use AI as a collaborative tool rather than a replacement for human insight. After generating content, review it critically for signs of bias — whether in language, perspective, or representation — and edit accordingly. Content Guardian Agents^SM help flag biased language or ensure compliance with inclusivity standards. But solely relying on technology isn’t advisable. You also need to actively engage in developing an awareness of common stereotypes or bias tendencies related to your field or topic, so you better recognize and correct algorithmic bias in AI-generated outputs.

3. Be specific in your prompts

Lastly, when creating content using generative AI, learn to be specific in your wording. Prompt your AI to include the perspective of marginalized identities and quiz it on its own answers using your knowledge of the biases listed in the above table.

Catching biased outputs with Markup AI

A service like Markup AI is your enterprise content insurance policy. Our Content Guardian Agents capture and digitize your style guide to make your writing standards… standard. And we’ve got you covered when it comes to catching discriminatory or non-inclusive language in your content. Over the years, we’ve helped our customers catch many examples of AI bias before publication.

With the right tools you adapt your processes to consider diverse backgrounds, cultures, identities, and experiences. This promotes a sense of belonging and creates a respectful, sensitive, and considerate environment for everyone.

Here’s how Markup AI tackles biased content:

1. Automated inclusive language checks: Markup AI’s Terminology and Tone Agents check for language that alienates or offends different demographic groups. This includes scanning for gender-neutral terms, avoiding stereotypes, and suggesting more inclusive alternatives. For instance, instead of “chairman,” Markup AI might recommend “chairperson.”

2. Customization for enterprise standards: Content Guardian Agents allow enterprises to set specific inclusivity standards that align with their values, and checks content against these custom guidelines, ensuring both compliance and brand alignment.

3. Immediate feedback: Writers get instant educational suggestions while creating content, allowing them to correct biased terms or language in their writing tool of choice. This ensures that content generated by both humans and AI aligns with the company’s inclusivity goals. 

For enterprises deploying generative AI content tools, Markup AI inclusive language checks are crucial in mitigating the risk of inadvertently publishing biased content. 

As an API-first platform, Markup AI allows enterprise teams to integrate guardrails into existing content workflows. This adds a necessary layer of quality control, especially as AI-generated content continues to scale rapidly. Find out how we can help your organization, let’s talk.