How Is AI Biased?

How human bias became algorithmic bias

The problem with algorithms started well before their invention.

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. Despite AI systems’ attempts at objectivity, these systems can perpetuate the same prejudices and inequalities we hoped they could overcome. 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.

This blog is an explores examples of AI bias — where it 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.

AI bias in content creation

AI bias can show 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 might struggle to accurately represent regional dialects or minority languages, reinforcing the dominance of majority cultures in content output.
• A generative AI that creates advertisements for tech products might disproportionately target younger users. That may overlook older demographics who are equally interested in technology.
• In marketing content, generative AI models may produce outputs that reflect stereotypical or outdated ideas. For example, when generating 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 can 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 bias examples and understand how it can introduce bias during the data collection process for Large Language Models (LLMs).

Examples of AI bias in algorithms

It’s helpful to learn how to identify what kind of biases exist. Giving implicit biases a name helps catch the specific cognitive shortcuts or prejudices that can affect their judgment. This awareness allows for more deliberate efforts to challenge these biases, leading to more balanced, inclusive, and objective outputs, whether in writing, hiring, or decision-making.

Additionally, naming biases gives people a common language to discuss and confront these issues in both personal and organizational settings. 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 shared understanding helps foster a culture of awareness and accountability, improving the quality of outputs across different contexts. Learn a few more below!

Type of bias	Description	Example
Training data bias	The data used to teach the AI is unbalanced or incomplete, leading to skewed results.	A hiring AI trained only on resumes from men might favor male candidates.
Algorithmic bias	The way the AI’s rules are written creates biased outcomes, often unintentionally.	A loan approval AI may be designed in a way that unfairly rejects applicants from low-income neighborhoods.
Selection bias	The data fed into the AI isn’t representative of the real-world population.	An AI trained on mostly lighter-skinned faces struggles to recognize darker-skinned individuals.
Confirmation bias	When AI is designed or used in a way that reinforces pre-existing beliefs or stereotypes.	A social media recommendation AI shows users only content that matches their existing views, creating echo chambers.
Interaction bias	The AI system learns bias from the way people interact with it.	A chatbot may learn offensive language if many users talk to it that way.
Cultural bias	When AI reflects the cultural norms or values of the group that created it, ignoring diversity.	A language AI may struggle with regional dialects or non-Western naming conventions.
Exclusion bias	When certain groups are left out of the AI’s decision-making process or results.	A healthcare AI might be less accurate for women or marginalized identities if the data used to train it didn’t include enough samples from those groups.

How to avoid bias when creating AI-generated content

It’s hard to know what you don’t know. But avoiding bias when creating AI-generated content starts with intentionally learning a myriad of examples of AI bias so you can recognize them when you review content. Paired with understanding the limitations of generative models.

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.

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 governance software (e.g., Acrolinx) can 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 can better recognize and correct algorithmic bias in AI-generated outputs.

Be specific in your prompts

Lastly, when creating content using generative AI, learn to be specific in your wording. Prompting AI to include the perspective of marginalized identities and quiz it on its own answers using your knowledge of the biases listed in this table in this blog. If you need help with crafting better prompts, why not check out our prompt guide? If you need to brush up on inclusive language, we’ve got a great place to refresh your memory in our inclusive language hub. 

Catching biased outputs with Acrolinx

Acrolinx is your enterprise content insurance policy. Our AI-powered content governance software captures and digitizes 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.

Inclusive language is a way of choosing words that are respectful, sensitive, and considerate of diverse backgrounds, cultures, identities, and experiences. The goal of using inclusive language is to promote a sense of belonging, eliminate discrimination, and create an inclusive environment for everyone.

Here’s how Acrolinx tackles biased content:

1. Automated inclusive language checks: Acrolinx checks for language that could alienate or offend different demographic groups. This includes scanning for gender-neutral terms, avoiding stereotypes, and suggesting more inclusive alternatives. For instance, instead of “chairman,” Acrolinx might recommend “chairperson.”​​
2. Writing assistance on inclusive language: Acrolinx’s inclusive language guidance integrates into the writing process across various tools and environments. It flags content with non-inclusive language and offers suggestions on how to improve it. This not only prevents biased content from being published but also trains writers to adopt more inclusive language habits​​.
3. Customization for enterprise standards: The platform allows enterprises to set specific inclusivity standards that align with their values, and Acrolinx checks content against these custom guidelines, ensuring both compliance and brand alignment​​.
4. 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​​. And writers don’t slip into bad habits, as Acrolinx reminds them to use inclusive language while they write. Bonus!

Acrolinx governs new and existing content written by people and generative AI. Whether your company has written 100,000 words or billions (like our customer Microsoft), Acrolinx makes sure each one reflects your style guide; not only for inclusive language! 

For enterprises deploying generative AI, Acrolinx inclusive language checks are crucial in mitigating the risk of inadvertently publishing biased content. It adds a necessary layer of quality control, especially as AI-generated content continues to scale rapidly​. Find out how we can help your organization here.