AI bias has become one of the most significant concerns in today’s data-driven environment. Dealing with it requires B2B brands to understand their data, scrutinize it while continually pursuing quality data.
Competing in today’s B2B digital marketing landscape requires B2B organizations to continuously leverage advanced technologies. This has led to a surge in marketing tools that are increasingly powered by AI. While AI-based tools can help to address human subjectivity, they can also demonstrate ingrained biases leading to inaccurate or discriminatory predictions for a specific subset of the target audience.
AI bias in B2B marketing has increasingly become a concerned area for CMOs. In fact, as per a 2021 report from Gartner titled “Gartner 2021 Digital Marketing Survey,” 75% of the respondents piloting AI/ML projects worry about trusting the technology.
For B2B brands to remove AI bias, they should not take AI algorithms at their face value. CMOs should ask themselves many questions about the ML and NLP that as imparted ‘intelligence to the artificial intelligence tool. They should reflect on their perspective and understand the impact of these biases on their strategies, based on their personal experience.
Here are four steps B2B brands can take to decrease the impact of AI bias on their marketing strategies and planning:
Assess the AI training data
Academic and commercial datasets are the top factors that often create bias in AI algorithms. Therefore, B2B brands should have data scientists who cross-train employees in various departments to know how AI bias works and the optimal method to address the problem. Additionally, having data onboard helps the marketing department to get a holistic view of the diversity associated with their buyers.
The constant pursuit of better data
In today’s data-driven world, B2B brands should continually seek better training data. They should ensure that their vendors are also on the same path.
Also Read: Lessons B2B Strategists Can Learn from 2021
CMOs should not restrict themselves to a specific data set. They should get more data, go broader and deeper, and always try new things to collect or capture data they can use to craft strategies. They should collaborate with their vendors to understand where their training data is coming from, identify inherent biases, and then they can control how it is being updated.
Scrutinize decision making of AI
Previously, manual lead scoring models enabled B2B businesses to inspect the bias present in them. However, this becomes difficult to identify in AI models, as it requires specialized skill to thoroughly understand them.
Dealing with AI bias requires B2B organizations to make it prescriptive while also being transparent. They should enable B2B buyers to review the features of AI to consistently deliver the desired business result.
Most definitely, NLP data that is fed into AI should also be checked by humans. B2B brands should enable their customers to review AI features in applications. When there is transparency in the utilization of AI, humans, and technology collaborate and hold each other accountable to mitigate bias in modeling.
As AI algorithms continue to grow more complex, it will only get difficult to eliminate data biases. However, with the incorporation of better data along with end-user interference, B2B brands can significantly lower the AI bias that continues to plague their marketing strategies.
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