Data and AI algorithm bias have become significant issues in growth marketing, with effects that are now evident in areas such as hiring, finance, and advertising. While the problem has been studied by data and machine learning scientists for years, recent high-profile cases have brought the real-world consequences of algorithmic bias into sharper focus.
In 2019, entrepreneur David Heinemeier Hansson publicly criticized Apple Card for assigning his wife a lower spending limit despite similar or better financial credentials. More recently, Workday faced a lawsuit alleging its AI screening tool discriminated against older and disabled job candidates. These incidents highlight how data and AI bias can impact outcomes beyond theoretical concerns.
Data bias arises when errors occur during the collection, processing, or analysis of information. Such errors lead to results that are skewed or unrepresentative. Although data bias existed before artificial intelligence became widespread, the adoption of AI has changed how these biases are recognized and addressed.
There are three main types of bias in AI:
Selection Bias: This occurs when an algorithm is trained on datasets that do not accurately represent the intended target group. For example, using only loyal customer data to build a recommendation engine may cause it to overlook patterns among new or infrequent customers.
Proxy Bias: Algorithms may use neutral-seeming data points as stand-ins for sensitive characteristics like socioeconomic status or age. This makes some forms of bias harder to detect and address.
Human Bias: The people involved in preparing training data can unintentionally introduce their own prejudices into datasets. For instance, if a team lacks diversity, generated images from an AI art tool might default to certain races or genders when depicting roles like “CEO” or “happy family.”
The presence of any type of data bias can affect growth marketing strategies by causing algorithms to make inaccurate predictions about users. In addition to poor targeting decisions, companies face risks related to brand reputation—such as negative publicity from biased ad campaigns—and legal exposure as regulations like the EU’s AI Act evolve.
Common types of data and algorithmic biases encountered by growth teams include:
1. Using Datasets That Include Negative Consumer Behavior: Relying solely on historical customer lists for tools like Facebook’s Lookalike Audience can reinforce confirmation biases and exclude potentially valuable new segments. Modern segmentation techniques recommend including diverse samples across all customer types and analyzing behaviors such as recency, frequency, monetary value (RFM), and lifetime value (LTV).
2. Platform Bias & Persona Validation: Testing products on platforms whose user base does not match the intended demographic can yield misleading results about target audiences. Marketers are encouraged to diversify channels in their go-to-market strategies and benchmark with search data to reach broader groups effectively.
3. Confirmation Bias in Marketing: Marketers often create content based on assumptions about ideal customers without questioning those beliefs over time. To avoid this pitfall, they should regularly review audience insights from platforms like Facebook and Google and allocate resources for exploratory campaigns aimed at identifying new segments.
4. Predictive Lead Scoring: Sales teams sometimes prioritize leads likely to close quickly rather than those who will deliver long-term value. Incorporating metrics such as Net Promoter Score (NPS) and potential product fit into lead scoring models helps ensure healthier business growth over time.
To reduce these risks, organizations should implement human-in-the-loop frameworks that combine company policies on ethical AI use with explainable models that marketers can audit easily. Ongoing monitoring is necessary because both user behavior and underlying datasets change over time.
“Algorithms are unable to imagine a future that is different from the past,” one expert noted. “Be mindful of where your data is coming from and if there is misrepresentation in your datasets.”
The article encourages businesses generating $3 million or more in annual revenue seeking help with managing data challenges in marketing efforts to consult with experienced agencies for support.
