AI bias is the unintended and harmful prejudices that can occur from data inputs into AI systems. Using AI with bias can impact campaign effectiveness, skew audience targeting, and harm a digital agency’s reputation. According to a report by SurveyMonkey, 39% of marketers are unsure how to safely use gen AI, resulting in misuse or avoiding the technology altogether. 

Therefore, it’s crucial to understand how AI bias works and how to mitigate these risks to ensure fairness, accuracy, and inclusivity in all marketing efforts. At Globital, we have been workshopping how to leverage AI tools in the best way possible to ensure agencies continue scaling without the associated legal risks. We explore the challenges of AI digital marketing ethics and law for digital agencies in our comprehensive webinar series. You can sign up for free for the Agency’s AI Advantage discussion to gain in-depth insights on the topic. 

1. Understanding bias in AI algorithms

Bias in AI refers to AI models making decisions based on systematic distortion or unfairness. These algorithms learn from data, and if the data they’re trained on is biased, the AI model will perpetuate those biases, leading to skewed results. The content output may subsequently favour certain demographics or perform audience targeting that excludes specific groups.

Examples of bias in AI

• Gender Bias: If a generative AI tool only creates male-centric headlines for a product or service launch, it could miss an entire audience of female-adjacent groups, resulting in lower engagement.
• Racial Bias: An AI tool that predominantly features lighter-skinned individuals in ad visuals could alienate diverse consumer groups and spark backlash, causing irreversible brand damage. 
• Socioeconomic Bias: If an AI tool is programmed to primarily focus on high-income segments for high-ticket clients, it may ignore middle-earning brands and individuals interested in upgrading, leading to missed revenue.

2. How AI bias affects digital marketing

• Impact on campaign effectiveness

Agencies today are increasingly embracing gen AI models to optimise digital marketing campaigns, from content creation and audience segmentation to ad targeting and more. If these models are trained on biased input, the subsequent campaigns can alienate certain population segments or land flat with intended audiences.

The result? Missed opportunities for revenue from promotional and marketing materials, decreased ROI, and missing out on a broader and more diverse audience that may have a strong interest in the advertised product or service.

• The risk to brand reputation

Today’s consumers are attuned to ethical issues. A brand that is seen as perpetuating bias, whether racial, gender-based, or otherwise, risks audience alienation and backlash on social media. Building a positive brand image from the ground up is straightforward, but once tarnished, it’s nearly impossible to rectify. 

3.  Sources of bias in AI models

Understanding where AI bias originates is crucial for mitigating its risks. There are two primary sources of AI bias: data bias and algorithmic bias.

• Data bias

Data bias occurs when the data used to train AI models is incomplete, unrepresentative, or skewed. For instance, if an AI system is trained on demographic data that lacks diversity, it will fail to generate fair, inclusive results. As Forbes states: “To mitigate, you must have diverse training data, bias detection capabilities, and a diverse team that audits generated content for fairness. Human oversight is imperative.”

• Algorithmic bias

Algorithmic bias occurs when the design of an AI system unintentionally amplifies existing societal biases. This could happen if the AI’s design prioritises certain factors over others, leading to discriminatory outcomes. Even if the training data is diverse, a flawed algorithm could still result in biased decisions. 

Lauren Petrullo from Mongoose Media says, “While AI can help our copywriters never cramp up, we must have a human quality assurance layer to evaluate everything that comes forth and fact-check because hallucination in AI is real.”

4.  Strategies for mitigating AI bias in digital campaigns

As a digital marketing provider, there are steps you can take to mitigate the risks of AI bias in your campaigns. These strategies can help keep your agency and clients safe while using AI mindfully:

• Diverse and inclusive data collection

The foundation of reducing AI bias in the data. To train AI models effectively, digital agencies must prioritise diverse and inclusive datasets that reflect a wide range of demographics, behaviours, and preferences. 

Invest in data collection practices that ensure the datasets represent all relevant customer groups, resulting in more balanced and inclusive results and improving campaign relevance.

• Auditing and testing AI systems regularly

Routine audits and testing of AI systems are crucial to ensuring bias-free outputs. AI models should undergo regular checks to identify any discriminatory patterns and address them before they negatively impact campaigns.

Implement automated testing tools or collaborate with AI experts to conduct these audits. Regular testing will help uncover biases that may not be immediately apparent, allowing you to make adjustments to ensure fairness and accuracy.

• Transparency in algorithm design

Encourage transparency in the algorithms you leverage for content creation, targeting, and decision-making. When agencies and their clients understand how AI models operate and the data they rely on, they can make more informed decisions and ensure that AI tools are used ethically. 

“Transparency is key when using AI in marketing. It’s imperative that we disclose AI assistance to our clients, especially regarding content creation, so they are fully aware.” – Lauren Petrullo

Working with transparent AI platforms can also provide agencies with insights into how algorithms function, which helps in identifying and mitigating any potential bias.

5.  Tools and resources to minimise bias

There are various tools and resources available to help digital agencies reduce bias in their AI-driven campaigns. These tools can assist in identifying potential biases in content, targeting, and algorithmic design.

• AI bias detection tools

Several AI bias detection tools exist to help digital agencies spot and mitigate bias in AI-driven campaigns. These platforms use advanced algorithms to analyse AI-generated content and targeting to ensure it is fair and representative. Some tools even provide real-time feedback that helps agencies adjust campaigns before they go live.

• Best practices for ethical AI implementation

When implementing AI in digital marketing campaigns, agencies should follow ethical guidelines to ensure fairness and inclusivity. This includes regularly reviewing AI systems for bias, using diverse datasets, and incorporating human oversight where necessary.

“AI should never fully replace the human touch. For content that requires a unique voice or sensitive topics, human intervention is a critical safeguard against biases and inaccuracies.”

At Globital, we are working closely with our Chief AI Officer to provide responsible and ethical AI-driven marketing practices that ensure agencies and their clients remain on the right side of AI digital marketing ethics and law. By partnering with us, you can get your foot in the door of a supportive AI integration journey to scale your agency and navigate the complexities of AI in digital marketing. Simply book a free consultation with our team, and learn how we can help your agency build fair, profitable AI-driven campaigns that drive real ROI