Jennifer Chase is chief marketing officer and executive vice president at SAS.
Responsible marketing is not new. As marketers, we have always been responsible for our marketing activities. However, changes in recent years have brought the concept of responsible marketing to the forefront. The pandemic has made us all digital consumers, flooding businesses with more customer data than ever before. At the same time, data privacy laws have never been stricter, while supply chain shortages, inflation and social discourse increase pressure on brands to market responsibly.
While many technologies are changing the marketing landscape, more recently, generative AI has sensationally made headlines.
There have been reports of AI-powered chatbots ranging from passive-aggressive to pathological, sometimes riddled with false information. Others have asked chatbots to compile academic-quality essays from simple prompts, leading to debate over what constitutes plagiarism. There are concerns that the technology could be used to spread disinformation, manipulate markets and influence elections.
But despite the hyperbolic media coverage and conjecture, it’s important to remember that there are positive applications of AI, and have been for some time. AI offers many opportunities to increase accountability in your marketing and advertising programs. An AI application may look like a black box, but it can be an open book, unlike human decision-making.
Ethical or unethical? AI is neither
Ethical artificial intelligence is a somewhat misnomer. AI is mathematics; it is neither ethical nor unethical. The correlation of two factors implies the probability of other factors. These are the use cases that are good or bad, responsible or irresponsible. Fortunately, as ethical marketers that we are, we already have principles that we respect. THE ANA Brand Coalition for Humanity is a good example of collaboration united in principle around the impact of brands as a force for good; a passion for more targeted and ethical marketing; emphasis on promoting diversity, equality, inclusion and belonging; the missions of nonprofit organizations to improve the world; a desire for social responsibility and environmental sustainability; an aspiration for gender equality in all marketing and advertising; a more transparent and secure supply chain; and a toxicity-free Internet experience.
Inclusivity: ensuring accessibility and including diverse perspectives and experiences
Often when we talk about inclusiveness, we are referring to race or ethnicity, sexual or gender orientation, age, or physical and mental challenges. The “black box” view of artificial intelligence suggests that biases can lurk inside the machine without being revealed. They are easier to flush out than human prejudices.
Ajay Agrawal, professor at the Rotman School of Business in Toronto and co-author of Prediction machines: the simple economics of artificial intelligence, say it like this in an interview:
“While you can’t open the black box and understand the details of how the neural network works, what you can do with AI, what you can’t do with a human, is ask an infinite number of questions and she will always answer,” he said. For example, perhaps a loan officer denies a mortgage to someone from a marginalized race, whether due to conscious or unconscious bias. “You might ask the loan officer, ‘Would you have turned down this loan if the person was exactly the same, except they were a different race?’ No human would admit, “Oh, yes, I would have given them that loan if they had been white instead of black.” But AI will.
Ultimately, it is marketers themselves who bear the responsibility for the inclusiveness of AI. Biased data and models mean biased results, and that falls squarely on the marketing team.
Transparency: explain and educate openly about use
Transparency about what data we collect and how we use it has become a major consumer concern. Startups working to help businesses manage customer privacy quintupled between 2018 and 2021.
For all their concerns about how their data is collected and used, most consumers don’t really understand the principles of data collection, sharing, or use. An explanation of the different types of data and how they are used is often buried in boilerplate user agreements. When customers understand the value exchanged between marketer and consumer and the business responsibly learns from their data, brand trust becomes relational, not transactional. And this has implications for long-term loyalty.
Accountability: proactively identify and mitigate negative impacts
AI offers a proactive approach to predicting and mitigating threats from criminal actors. Given its enormous volume of information about exploits and vulnerabilities, it can reverse engineer an attack before it occurs. But when a failure occurs, companies must be the first to announce the news and develop a plan to keep consumers safe. This preparation also demonstrates the remaining principles of AI development: the resilience to survive anything from a coordinated attack by bad actors to a simple node outage; and respect for the privacy of all those involved.
Responsible AI can also drive accountability in an organization proactively. The Bissell Center in Edmonton, Canada, has had a mission since 1910 to alleviate the impact of poverty. in the northwest city. It has recently turned to comprehensive AI data analysis to achieve better results and to keep the success of its programs and the need for adequate funding at the forefront among political and other stakeholders.
The future of AI as a tool for consumers and brands, rather than a threat to them, is bright if we take a principled approach, both as technologists and marketers. Used strategically, AI is no different from any technology: it manages the work that can be done by a machine while allowing humans to focus on greater value-adds that require judgment and of human creativity. Without this, we risk losing productivity. AI is changing the world. It doesn’t conquer the world. It gives humans the means to create a better world.
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