Artificial intelligence (AI) and machine learning have become a critical component of any organization’s marketing strategy. They help improve efficiencies, deliver personalized customer experiences, and drive positive performance. As we continue to lean on AI more, it’s imperative that we oversee it in a way that promotes transparency, responsibility, and equity.  

That’s where ethical AI comes in. Ethical AI, sometimes called responsible AI or explainable AI, is about understanding where biases may exist in our current framework of data and customer analytics and adjusting the equation to minimize any unintended harm to specific groups. It means treating each person in our datasets as a unique individual rather than a collection of demographic attributes.  

Beyond its inherent value as a fairer, more customer-centric way to advertise, there are three key reasons that an ethical AI strategy should be top of mind for your organization in 2022. 

1. Accurate representation is always better

Study after study has found that more diverse viewpoints lead to better outcomes. The same holds true when it comes to AI. When you don’t know what’s happening inside of the black box, you might be unintentionally reaching just a fraction of your relevant audiences. If you’re not communicating with diverse groups of individuals, pockets of potential customers are missing the chance to benefit from your products and services. As a business, you miss out on their critical feedback, including their behaviors and reviews. Reaching a diverse, representative audience benefits everyone. 

Let’s consider an example where you’re using AI to promote a new pickup truck – a product that’s historically been heavily marketed to males. In this example, your AI is only considering demographics and historical data (which, as you might guess, is also heavily biased to overrepresent males) to make decisions instead of looking at behavioral attributes. This is suboptimal in a few ways: 

• Female consumers are missing out on the opportunity to learn about a product that could provide what they’re looking for in a new vehicle. 

• The company is missing out on females’ perspectives about how the product uniquely meets their needs and how it could be improved. 

• The cycle gets perpetuated – the data inputs are all based on males’ experiences, so the product continues to overwhelmingly attract males, the AI continues to market to only male consumers, etc. 

The cycle needs to be broken to improve the user experience and increase prospective customers for the business. 

2. Consumers want to buy from brands they trust 

The societal and cultural events of the past two years led consumers to place a higher priority on authenticity, trust, and diversity for the brands they engage with. Ethical AI and transparent AI processes can create positive consumer sentiment in all three of those areas. Ethical AI, when communicated appropriately, lets a user know that a company will market to them and others in a responsible way. It also signals a brand’s commitment to diverse and inclusive processes, building consumer trust. That trusting relationship can pay off from a business perspective - eighty-two percent of US consumers say they will continue to buy from a brand they trust, even if another brand suddenly becomes hot and trendy. At a time when consumers have hundreds of options at their fingertips, trusting relationships are immensely valuable and will serve your brand now and in the future. 

3. Understanding your modeling can uncover underperforming processes 

Machine learning and AI are smart, but they still need human oversight to make sure they’re operating to their full potential. In fact, their sophistication means that they require more oversight than more straightforward methods of modeling and segmentation.  

That understanding of how your algorithms operate is foundational to ethical AI, and can uncover processes that aren’t optimal, either from a representation or performance standpoint. When you pull back the curtain, you may find that an AI process is leaning heavily on demographic data to make decisions. Ideally, you want behavioral data to be the driver of AI. A person’s identity as a travel enthusiast, dog lover, and amateur chef is much more relevant for personalizing content than the person’s categorization as a 36-year-old male. Understanding how the sausage is made means you can add and remove ingredients to get the recipe right.  

 
Artificial intelligence is markedly changing the business and advertising worlds – but with great power comes great responsibility. Building and executing on an ethical AI strategy is a critical endeavor to better connect with the entire customer universe, build consumer trust, and ultimately improve business outcomes.