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What is Ethical AI?

The role of artificial intelligence (AI) in marketing continues to expand as new capabilities become available. At the same time, businesses have an increasing desire to cast light on the black box to understand how AI is operating. Marketing and analytics stakeholders – as well as consumers – want to make sure that they’re spending ad dollars in a way that’s thoughtful and transparent. These conditions have led to the rise of ethical AI – but what exactly does “ethical AI” mean? 

Ethical AI is About Responsibility 

As a nascent concept, definitions of ethical AI vary across organizations. In fact, ethical AI is a bit of a misnomer, because it implies that current AI is not ethical. A more appropriate term may be responsible AI. At its core, ethical AI is about using artificial intelligence responsibly, in a way that does not discriminate against or cause harm to any stakeholders of the organization. It means creating a clear framework of ethics and holding your AI processes to those standards. Those ethics should include legal requirements around topics like privacy as well as values that are important to society.   

The Role of AI in Marketing 

The field of artificial intelligence was born in 1956, when 10 scientists and mathematicians met at Dartmouth College to explore the idea of programming machines to use language and solve problems for humans while improving over time. In the 66 years since then, artificial intelligence has permeated into many aspects of business, helping organizations improve internal processes and customer experiences. Businesses use AI for marketing measurement, customer experience optimization, audience targeting, and more. It has truly transformed the way organizations operate, but there’s still room to improve the ways in which businesses use and oversee AI. 

Ethical AI Addresses Common Challenges of Current AI Usage 

Many organizations currently lack an understanding of how their AI-based processes operate. There are several key problems that arise from working this way. 

  • AI contains biases that can perpetuate and self-reinforce if they’re not addressed. If algorithm inputs are biased toward white males, for example, then that group will be disproportionately advertised to, the resulting data collection will heavily lean toward white males, and the new inputs will also be biased – and the cycle continues. 

  • A lot of those biases occur because of the inputs organizations use. Current AI, and businesses more broadly, frequently rely on demographic data more than behavioral data because it’s easier to collect and segment. However, demographics alone do not appropriately paint the picture of a unique individual.  

  • In combination, these issues mean that current AI does not always maximize business outcomes. Biased audiences can miss out on entire segments of valuable prospects. Targeting and messaging that don’t account for a person’s likes, interests, or behaviors are not as relevant as they could be. Consumers and brands alike are left wanting more with current AI. 

Ethical AI addresses these shortcomings by guiding the algorithm and its inputs to follow an established ethical framework. Understanding how the AI operates leads to greater transparency and the ability to interrupt the self-reinforcing cycle. Ethical AI also requires leaning more on behavioral data over demographic data, creating opportunities to build first-party data strategies that make consumer interactions more human. 

Over the coming months, we’ll feature a series of blog posts to dig further into ethical AI and how you can begin to incorporate it into your organization. Keep an eye out for our next post addressing three key reasons ethical AI needs to be on your roadmap.