As brands work to optimize for AI-driven answer engines, it’s critical to address the ethical dimensions of this new landscape. AI models can carry biases, omit important perspectives, or even spread misinformation. For marketers and SEO professionals, there are two sides to this: how to ensure your brand is treated fairly by AI (not excluded or misrepresented due to bias), and how to engage in GEO practices responsibly (not contributing to bias or misinformation). In this article, we’ll explore both angles and provide guidance on navigating bias and ethics in Generative Engine Optimization.
Understanding AI Bias and Its Impact on Brands
“AI bias” refers to systematic preferences or prejudices in an AI’s outputs that aren’t intentionally programmed but arise from training data or model design. Commonly discussed biases include political bias, gender or racial bias in content, etc. But how does bias manifest in search answers and brand visibility?
- Popularity Bias (Rich-get-richer): LLMs trained on internet data will “know” popular entities better than niche ones. This means if you’re a smaller brand, an AI might default to mentioning the big players (simply because they appeared more often in the training data). This bias can result in a lack of diversity in answers – e.g., always citing the same top 3 companies. From an ethical standpoint, it can reinforce market incumbency and stifle newcomers (the opposite of what a neutral search should do). As a brand, you combat this by increasing your presence (as covered in frameworks), but it’s also something AI developers are aware of and may try to counterbalance by explicitly injecting variety.
- Geographical/Cultural Bias: AIs might lean towards content from certain regions (often US/Europe, due to data availability). If your brand is big in Asia but most English web content skews Western, an AI might not mention your brand in a global answer. We’ve seen analogies in early voice assistants that struggled with non-Western names – similarly, an AI might omit a non-English brand name for a query about global companies simply because it didn’t see it as much. This calls for ensuring there’s cross-language content about your brand and perhaps engaging in English-language PR even if your primary market is elsewhere.
- Data Source Bias: If an AI relies heavily on certain sources (say Wikipedia or a specific knowledge graph), it inherits their biases and omissions. For instance, Wikipedia has notability guidelines – if your brand didn’t meet them and lacks an article, the AI might infer you’re not notable. Ethically, this means notable entities could be missing from answers. Brands have sometimes tried to create Wikipedia pages to “fix” this, but doing so in violation of Wiki guidelines is unethical. A better approach is to legitimately earn a presence there (third-party coverage leading to a Wiki entry).
- Algorithmic Bias vs Human Bias: Some biases come from the model (e.g., the resume screening bias where ChatGPT ranked resumes with disabilities lower. If an AI, for example, scours reviews and finds frequent mentions of a stereotype (like “women’s bikes are not as high performance” – just as a hypothetical), it might incorporate that bias in an answer about best bikes, inadvertently favoring men’s models. Translate to brand: if there’s biased chatter (maybe unfair negative press or stereotypes about a brand’s category), the AI might reflect that. Brands need to watch for these and correct the narrative with facts. If an AI answer contains a biased or false statement about your brand (e.g., implying your product is not for a certain group without basis), that’s a serious issue to address.
Impact on Brands: Biases can lead to your brand being left out or presented in a skewed way. For example, if AI has a bias towards “open source” in tech answers, a proprietary software company might rarely get mentioned even if it’s objectively a leader. Or if there’s gender bias in how products are described (there have been cases where AI would describe leadership of companies differently based on gender), a female-led brand might not get the same credit (“AI assistant calling a male CEO ‘visionary’ but a female CEO ‘caring’, for instance, reflecting training data bias).
Brands need to be vigilant. One actionable step is bias testing: ask the AI about your brand and competitors across different contexts. Do you notice any pattern like it always highlights one competitor’s strength and downplays yours? Is it using language that carries bias? If so, you can take steps: update how information is presented on your site (maybe the AI is picking up wording from your own materials that inadvertently self-sabotages), or provide better context in public-facing content.
Ensuring Your Brand is Fairly Represented
To navigate bias and non-inclusion:
- Audit AI Outputs for Your Brand: On a schedule, review how AIs answer questions about your brand or category. If you find inaccuracies or concerning omissions, document them. For example, if an AI says “Brand X is not available in Europe” and that’s false, that’s a problem.
- Provide Corrective Data: If misinformation or bias exists, counter it with factual content. This could be a blog post clarifying a misconception, or getting a correction issued in a publication that the AI might be referencing. One interesting approach is using the AI’s own feedback channels: if Bard or Bing provides a wrong answer about you, use the feedback option to flag it. If ChatGPT says something incorrect, correct it in the conversation (this data might feed back into model improvement).
- Diversity in Content: Ensure your content (and the content you contribute elsewhere) reflects diversity and inclusivity, especially related to your brand. For instance, if you have testimonials or case studies, include a range of customer backgrounds. This not only is ethically positive, but it may help the AI present a more balanced view of your brand’s user base or use cases.
- Opting Out vs. In: There’s an ethical decision about whether to allow your site to be used in training data (via robots.txt directives to AI crawlers). Opting out might protect your content from being used without permission, but it also means the AI might not “know” your site at all – thus not mention you. Some brands (especially publishers) are wrestling with this. From a pure GEO view, opting in (allowing crawling) increases inclusion chances. But it has to align with your business’s stance on content usage. It’s a strategic decision: do you value potential traffic/mentions over the IP use of your content? There’s no one right answer, but consider the trade-off.
- Engage in the Conversation: Just as SEO pros engage with Google (through webmaster forums, conferences, etc.), engage with the AI companies if bias affects you. Some have feedback programs or AI councils. For example, OpenAI has solicited input on improving factual accuracy and reducing bias. Contributing your experiences (e.g., “Our company is often miscategorized by AI as doing X when we do Y”) can both put your issue on their radar and show you as a proactive, good actor.
Ethical GEO Practices for Brands
Now, turning the mirror: as you optimize for AI, ensure your tactics are ethical:
- Don’t Manipulate or Deceive: This might sound obvious, but the temptation might arise to try to “fool” the AI with false content or astroturfing. For example, an unethical approach would be to publish dozens of blog posts or forum comments with misleading praise for your brand hoping the AI parrots it. Not only is this against most platform policies, it can backfire if discovered. It’s akin to black-hat SEO (like link farms) – and we know how that ends. AIs will get better at detecting unnatural patterns. It’s better to have genuine signals even if fewer, than a bunch of phony noise.
- Respect User Privacy: AI search will sometimes involve personalized answers (especially if a user’s context is known). If you are feeding content into systems or building your own chatbots, handle data with care. Also, be transparent on your site about how content might be used by AI (some companies now have disclaimers like “Content on our site may be used to train AI models”). While not required, it’s part of building trust.
- Avoid Bias Amplification: Be mindful that your content doesn’t inadvertently reinforce biases. For instance, if all your examples in content are one demographic, an AI might present your solutions as only for that group. Internally, train your content creators on inclusive writing. This is both ethically right and ensures you’re not feeding the AI skewed input about your brand or domain.
- Accuracy Over Spin: In traditional marketing, there’s a lot of “spin” – highlighting positives, downplaying negatives. In AI answers, spin can turn into misinformation if you’re not careful. If your product has a limitation, better that you acknowledge and address it in your content, rather than have the AI pick up a random forum complaint as the only mention of that limitation. Honesty in content means the AI is more likely to give a fair and correct answer. Brands like Volvo, for example, became known for safety by openly talking about and improving from failures. If an AI gets asked “Is [Your Brand] reliable?”, and your content includes transparent discussions of reliability and improvements, it’s more likely to reflect well than if it finds only customer complaints and no response from you.
- AI in Your Content Creation: Many brands are using AI to generate content. This raises ethical issues too – like disclosure (some sites mark AI-generated content), and quality (AI can inadvertently produce subtly biased or plagiarized text). If you use AI to help create SEO/GEO content, have human review and fact-checking. The last thing you want is your site spreading an error that then gets picked up by another AI; you create a vicious cycle.
The Responsibility of Being Cited
Imagine your content becomes the go-to answer for an AI on a certain question. That’s great for visibility – but it also means people may act on that information without ever visiting your site. This raises the bar for accuracy. If an AI says, “According to [Your Company]…,” and gives advice, and that advice leads to a bad outcome, it could reflect poorly on you (even if the user never saw your detailed article with all the caveats).
Thus, an ethical approach is to:
- Provide Context: If an answer has nuances (e.g., a medication is good for most but not all conditions), make sure your content clearly states those nuances upfront. AIs sometimes truncate or summarize; important qualifiers should not be buried. For instance, start a paragraph with “One warning: this medication is not recommended for children…” rather than hiding that in the middle.
- Keep Information Up-to-Date: If you see an AI citing your two-year-old article as of it’s current, and things have changed, update that article (and date it). AI might not catch every update, but over time it will pick up the new info. Ethically, updating reduces misinformation.
- Be Ready for Feedback: If increased visibility via AI brings new feedback or criticism (people will say “I asked ChatGPT and it cited your blog and I think it’s wrong!”), handle it gracefully. Don’t blame the AI – verify if your info was outdated or misinterpreted and respond accordingly. Brands that show they care about correctness will earn trust in the long run.
Pushing for AI Fairness and Inclusion
Brands, especially those who are not industry giants, have a stake in AI fairness. It may be worth joining or at least following industry groups that work on responsible AI and search. For example, the Partnership on AI has committees on responsible practices. While that’s more high-level, the outcomes (like guidelines for AI citation, or standards for content sourcing) could directly affect how easily you can influence AI results.
Also, consider the diversity of voices in your content. If you run a content platform, include diverse contributors. If you have data, ensure it’s collected and presented without bias. These efforts not only improve your brand image but contribute to a richer dataset for AI to learn from, which benefits everyone.
In summary, brands should approach GEO not just as a game of visibility, but as a part of a broader ecosystem of information. With great visibility comes great responsibility. By striving for accuracy, fairness, and transparency in your content – and advocating for those values in AI outputs – you not only help your own cause but also the end-users who rely on these answers.
As AI becomes a primary interface for information, brands that build trust will stand out. And trust is earned by consistent ethical behavior. The short-term gain from any shady tactic is far outweighed by the long-term risk to reputation (with both humans and algorithms).
Next, we will shift gears to discuss what this all means for careers in marketing and SEO. With GEO rising, what new roles or skills are emerging? How can professionals prepare? Let’s explore the career advice for thriving in this evolving field.