An Approach To Mitigating AI Bias in Transforming Marketing Operations

As marketers and advertisers utilize AI to transform their businesses, understanding the potential for bias and mitigating procedures is critical. To harness the power of AI for good, organizations should be asking themselves these seven important questions, says Ingrid Burton, CMO, Quantcast.

Today, artificial intelligence (AI) plays a particularly prominent role in marketing and advertising efforts. Data is generated constantly in the digital sphere, including business data, real-time customer engagement data, web interaction data and more. This gives organizations the opportunity to apply AI and Machine Learning (ML) on this wealth of data to draw new insights that can be used to improve and reshape marketing and advertising campaigns and drive better business results.

At a fundamental level, AI helps marketers and advertisers better understand their customers and their business. When using the technology for these purposes, organizations are placing incredible trust in AI — trust that AI is providing accurate insights that guide optimal actions. Some people, especially in less technical roles, may occasionally think that emerging technologies like AI are nearly infallible, free from the sort of errors common in human decision-making. But AI can be marred by bias, including even the best AI models. For marketers and advertisers, biased AI harms efforts to learn more about customers and reach the right audiences. To be confident that they can trust AI, organizations must understand how AI bias impacts them, how they can avoid AI bias incidents and what to do when bias incidents occur.

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The Impact of AI Bias on Marketing and Advertising

There are many ways in which bias can slip into data, including under-sampling certain demographics, broader sampling errors, or even basic human conscious or unconscious bias. When AI and ML technology is applied to data that is inaccurate, it has the potential to exacerbate the errors and cause even more bias in the system.

Bias in AI can lead to poor marketing and advertising decisions that severely impact results, such as failing to reach the correct audience or serving up the wrong ads to a certain demographic. When using AI to determine which audience you’re reaching, if there’s bias, you can never be sure that you’re reaching the right users. Biased AI can cause marketing and advertising campaigns to miss an entire segment of people, which means organizations lose the opportunity for sales, make new customers or drive brand awareness. Bias ultimately means wasted money and resources, failure to reach relevant audiences, and potential reputational brand harm from serving ads to the wrong people.

A Checklist for Avoiding AI Bias Incidents

I recently teamed up with Patrick HallOpens a new window , principal scientist at bnh.aiOpens a new window , to lead a webinarOpens a new window  about AI bias. Patrick is an expert on avoiding, detecting, and responding to AI liabilities. During the event, he laid out seven key questions organizations must ask themselves to ensure they’re doing everything they can to mitigate AI bias:

• Fairness: Are you observing any variations in outcomes or accuracy among different audiences?
• Transparency: Do you understand how your model makes its decisions?
• Negligence: Are you protecting end-users? What are you doing to make sure your AI is safe and reliable?
• Privacy: Do your AI models adhere to all privacy laws and your company’s own privacy policies?
• Agency: What are you doing to prevent your models from making decisions that haven’t been approved by your organization?
• Security: Can you quickly and reliably identify any breaches? Does your AI system leverage relevant security standards?
• Third parties: What third-party tools does your AI system leverage? Does it rely on any third-party services or personnel? How do these third parties impact any of the above considerations?

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Prepare for AI Bias Incidents

Some level of AI bias incidents is inevitable. Because of this, organizations need to be prepared to solve any problems that emerge. They need to adopt effective detection systems and best practices; create processes for mitigating bias incidents; develop a method for determining how and when incidents are fully remediated, and learn from every incident to avoid them in the future. Finally, the best practice is to document processes, models and outcomes.

AI Transformation in Marketing and Advertising

AI has the potential to transform marketing and advertising efforts for the better. When the technology is used correctly, it can provide valuable, actionable insights and intelligence about a business’s customers and potential customers. Using these learnings, marketers and advertisers can significantly boost key business metrics, such as reducing customer acquisition costs, improving lifetime spending per customer, delivering the right offers, and driving increased revenue in general. But biased AI can lead to a skewed understanding of customers, hurting these metrics and potentially harming your brand. Therefore, understanding and guarding against bias in AI will lessen its impact on your advertising and marketing so that you can maximize your audience reach and boost business outcomes.