In The Quiver

Generative AI Bias in B2B Marketing: What to Look for & How to Manage It

Generative AI (GenAI)-powered technologies are quickly becoming an integral part of B2B marketing content generation. McKinsey & Company estimates about 75 percent of the value GenAI could deliver falls across four functional areas, one of which is sales and marketing. And recent Deloitte research notes that 63% of the “most effective content marketing leaders” saw an increase in annual revenue by making GenAI part of their content supply chain strategy.

Given these figures, using GenAI for content sounds like a no-brainer. But as recent high-profile examples have shown, GenAI has its drawbacks as well.

AI bias: Beware trading quality for quantity

As recent examples in the news have shown us, AI bias is a practical reality, and B2B marketers should take note. Before you commit to using GenAI for mass content generation, it’s important to understand the nature and scope of the problem.

Why is AI bias even an issue? To put it simply, AI systems aren’t born intelligent: they’re trained on data sets. And the data sets used to train them can be mildly — or extremely — biased. Following are some examples of common types of AI biases and their impact:

Language and cultural: By over-representing certain languages or cultural perspectives, AI-generated content may resonate less with diverse global audiences, translating to missed opportunities.
Gender: Marketing content could unconsciously favor one gender over another, potentially alienating a significant portion of the target market.
Socioeconomic: Favoring narratives or scenarios that don’t include certain socioeconomic backgrounds can limit relatability and engagement.
Ethical and moral: Content that doesn’t align with the values of clients or the general public may raise concerns about brand integrity.

The good news is that some big players are working to help mitigate the inherent risks of AI bias. One example: IBM has made significant strides by integrating AI Fairness 360, an open-source tool suite designed to help users detect and mitigate bias in AI models. This enhances the accuracy of AI applications and helps reinforce trust among users’ B2B clients by ensuring equitable outcomes. Similarly, Salesforce’s “Einstein AI” incorporates ethical AI practices focused on training models with diverse data sets, continually monitored for bias.

Best practices to mitigate bias in AI-generated content

For most businesses, implementing AI will require a bit of “organizational surgery.
But for starters, to help mitigate bias and safeguard the trust of clients and prospects, consider the following best practices:

Use diverse training datasets that account for a wide range of languages, cultures, genders, socioeconomic backgrounds, and ethical perspectives.
Continuously monitor and evaluate to help ensure biased language or messaging gets flagged. Have processes in place to address and correct any instances of bias, ensuring inclusivity and integrity in their brand image.
Engage diverse teams of experts in AI, marketing, and diversity to help identify and address any potential biases in the content creation process and make overall content more universally relatable.
Be transparent about use of AI technology in creating marketing content.

Even with a small marketing team, you can still mitigate AI bias

If you’re dealing with external tech providers, ask whether their technology has been developed with a diverse data set — and choose a vendor that’s transparent about their efforts to mitigate bias. Also, ask about their mechanisms for feedback and updates to the AI system. A vendor committed to continuous improvement will have processes in place to regularly update the AI algorithms, incorporating feedback to address and reduce bias over time.

If you’re managing your GenAI technology in-house, be sure to:

Leverage open-source tools and resources: Many open-source AI tools and platforms offer resources for training AI systems. Smaller businesses can use these tools to ensure their AI technologies are exposed to diverse data sets without a hefty investment.
Partner and collaborate: Forming partnerships with other businesses or research institutions can provide access to more diverse datasets and AI expertise. Collaborative efforts can also spread the cost and resource requirements, making it more feasible for smaller teams.
Implement manual checks: If your business has a smaller volume of content, manual reviews of AI-generated marketing materials may be more feasible. This allows you to identify and correct any biases, ensuring that content remains inclusive and diverse.

The potential impact of GenAI bias on your brand reputation and marketing success is real. But by taking a proactive approach to managing AI bias, you can mitigate the risks and capitalize on the benefits GenAI has to offer.

Looking to explore what GenAI can do for your digital marketing? We can help. Speak with one of our archers to learn more.

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