In building our customer data management software, we wanted to offer Shopify eCommerce customers something different than your regular marketing customer data platform (CDP) with AI-driven customer personas to add a greater dimension to your personalization execution. Despite being just only a couple of subscription software services (SaaS) to offer customer personas. Then when speaking to my advisors, one asked me, "what would happen to the world if the business didn't exist" and that is inclusion. Despite enterprise software users being nearly 50% female, less than 2% of enterprise software funding goes to women entrepreneurs. So, where are all the founders with no-Bias as a business priority and give customers what they need most- trust in their data? When you leave negative biases in the data at the business level, you can make all your predictive models inaccurate or even useless.
More existentially, to what extent does having only privileged males who have always dominated the industry beneficial to AI technology in the future? The impact of AI can be catastrophic to society because it amplifies the biases of these privileged males and will lead to unjust consequences- perpetuating privilege to only a small class. Think about what has already happened with facial recognition. How can facial-recognition software be sold as "market ready" in the United States if it cannot distinguish African-Americans as well as it distinguishes White Americans? Things must change.
Bias Marketing AI
At the heart of it, data segmentation is what marketing has been doing for years. The difference with Artificial Intelligence (AI) is that it can do it at scale and do it quickly. But, as we've seen, bias can creep in at many stages of the deep-learning process and the standard practices. Data scientists are trained in data handling practices, but what about the Marketer? We'll keep it light, so read on to learn more! We'll also share some tips for recognizing and avoiding bias in your marketing efforts.
Artificial intelligence is increasingly becoming a part of our everyday lives, but its rapid development has a dark side. AI bias is one of the biggest challenges facing technology today. This problem occurs when AI systems display a pattern of unfairness or discrimination in their decision-making. Unfortunately, bias is very hard to detect and fix. This article will explore how AI bias happens and why it's so difficult to overcome, but this doesn't mean we should start and try to solve these problems.
"'Fixing' discrimination in algorithmic systems is not something that can be solved easily. It's a process ongoing, just like discrimination in any other aspect of society." - Andrew Selbst, Data & Society Research Institute Post-Doctorate.
How AI bias shows up in marketing
One way that bias can enter into AI systems is through segmentation. Segmentation is dividing a population into groups based on shared characteristics. Customer segmentation is done for marketing purposes, targeting specific customers, or grouping people together for research studies. However, segmentation can also lead to bias if the created groups are not representative of the overall population. For example, if a segmentation algorithm only includes people who have purchased a certain product in the past, it will be biased against those who have never purchased it. It would be best to be careful how you frame the problem for AI to solve and provide it with the greater universe to consider.
Another way that bias can enter AI systems is through customer data. Customer data is often used to train and develop artificial intelligence applications. Selbst calls the problem a "portability trap" where systems are not designed to do different tasks within different contexts. In enterprise software consulting, process consults would go into the business and understand the specific business context to design a system on their behalf. This better practice is less used now as automation comes "in a box" for the ultimate efficiency in software acquisition. However, this data can be biased if it is not collected properly. For example, if a company only collects data from its most loyal customers, the created AI system will be biased against people who are not loyal to the company. You don't want bad data outcomes impacting your customer acquisition strategy.
For our customers, VIEWN cares about data ethics. Data collection causes bias by removing errors caused by human handling, resulting in using populations that are unrepresentative of reality. How do other customer data platform companies worry about bias in your customer acquisition strategy?
Bias can also occur during the development process of an AI system. The development team introduces bias with their own personal biases that can influence how they design and build AI applications. For example, if a developer unconsciously believes that one race or gender is superior to another, they may inadvertently create a bigoted AI system. This can be a problem because you can leave out great customers to your brand and miss out on big opportunities that come with inclusivity. Marketing Leaders, Data Scientists, and Product managers need to be a check for the engineering team. Product management has the requirements of the customers, employees, and business regulations.
Fixing bias in AI-enabled marketing analysis systems is no easy task. It requires careful planning and design at every stage of the process, from segmentation to customer data collection to model development. Even then, there's no guarantee that bias won't show up. By being aware of the problem and taking steps to prevent it, we can start to make a difference.
The bottom line is that data science and AI hold much promise for marketers, but we need to be aware of the dangers of bias creeping in. VIEWN is committed to helping our clients avoid these pitfalls and create successful marketing campaigns through the use of AI. If you're interested in learning more about how VIEWN can help your business or want to get started with AI-powered marketing, contact us today!