Artificial intelligence (AI) and machine learning (ML) have rapidly evolved from novel concepts to necessary components in brand marketers’ toolkits. Salesforce reported 68 of marketers say they have a fully defined AI strategy, up from 60% in 2021 and 57% in 2020. It’s a key piece in predictive modeling, campaign personalization, CX optimization, and nearly every other facet of marketing.
But AI has its shortcomings, as many brands have discovered over the years. In 2019, a software developer discovered that the algorithm behind Apple Card was inherently sexist. In the same year, the fintech industry faced backlash for discrimination in mortgage lending and home refinancing for people of color. While the two incidents have little in common on the surface, they trace back to a shared root cause—data bias.
So what is data bias? And how can you avoid falling prey to something that even two titans of technology struggled with? By recognizing what leads to data bias and preventing it before it happens.
One way data bias happens is when you train a machine learning algorithm with a dataset that isn’t properly representative of its intended use. For example, if you’re marketing luxury spirits, but only use data that reflects the behavior of beer drinkers to train your AI, you’re going to end up with heavily skewed and inaccurate results.
To avoid these issues, you need to understand the types of data bias and how they occur. While there are a variety of nuanced ways bias can creep into your data, these are seven of the most common forms:
1. Selection bias: Like our earlier example, selection bias occurs when the dataset used to train an algorithm either isn’t large enough or doesn’t properly represent the overall population.
2. Demographic bias: Demographic bias happens when the data used to train an algorithm is heavily weighted to a subset of the population. Racial bias is a common example of this, where visual-recognition algorithms are trained with video or images of Caucasian people and then fail to properly detect individuals with darker skin complexions.
3. Measurement bias: By training an algorithm with data that isn’t measured or assessed accurately, you’ll end up with measurement bias. If your brand sells software that runs on both Windows and macOS, but you only train your ML algorithm with data from Windows users, you’ve introduced measurement bias.
4. Recall bias: Recall bias is a specific form of measurement bias where inconsistent and subjective values lead to data variance. For example, if you ask a group of customers how often they’ve seen ads for your brand over the past month, they’d struggle to provide an exact number. Instead, they’ll estimate the frequency—often incorrectly—leading to skewed data.
5. Association bias: Association bias occurs when an algorithm picks up on correlations that are happenstance and treats them as fact. Imagine using a training dataset where only men purchased black cars and only women bought white cars. The algorithm would believe—incorrectly—that women never purchase black cars because the data reflected that bias.
6. Observer bias: Also known as confirmation bias, observer bias happens when you impose your opinions or desires on data, whether consciously or accidentally. For example, if you’re hoping to find that your brand appeals to as large of an audience as possible, you might subconsciously skew data to reflect that outcome.
7. Exclusion bias: Cleaning up data and removing outliers is an important step in preparing to train an algorithm. However, if you remove something important that you thought was extraneous, you can introduce exclusion bias. If the vast majority of your customers are American, you might be tempted to exclude data from other countries. But what if British customers spend twice as much as their American counterparts? That exclusion bias could be costing your brand money.
Biased data is bad in and of itself, but the downstream implications are far worse. As the saying goes, “Garbage in, garbage out.” Data bias can impact everything from campaign setup and ad buys to cost analysis when deciding whether to maintain or kill a program. In fact, respondents of a Forrester Consulting survey estimated they wasted over 20% of their marketing budget due to poor data.
For brand marketers, there are some specific ways data bias can wreak havoc:
Data bias is a huge problem for brands. Especially as marketers become more reliant on predictive algorithms and complex, AI-driven analytical tools. So how do you avoid the perils of data bias, and address it when it does crop up?
There are several processes and practices you can implement:
Debiasing data can be a daunting, imperfect process. You might even be wondering why you should bother. It’s a high-effort endeavor, but the costs of ignoring it can be far worse. Whether it’s hefty privacy-noncompliance fines or dwindling ROI, data bias can—and will—hurt your brand.
Debiasing needs to be part of your brand’s data strategy, and Scuba’s customer intelligence platform can help:
Data debiasing is an ongoing process, not a one-time box to check, and Scuba can make it as painless as possible.
Interested in learning more about how Scuba can help manage and leverage your brand's data? Request a demo today or talk to a Scuba expert.