Data Bias: Why It Matters, and How to Avoid It

By Megan Wells

Artificial intelligence (AI) and machine learning (ML) have rapidly evolved from novel concepts to necessary components in brand marketers’ toolkits. Salesforce reported AI use in marketing grew almost 200% between 2018 and 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.

Data bias is everywhere

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.

The problem with biased data

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:

 

  • Missed opportunities: The digital world moves at light speed and you can’t afford to make decisions based on biased data. It can lead to missed opportunities for conversions, upsells, and retention, as you and your team operate on flawed insights.

 

  • Skewed customer journey insights: Understanding your customers’ journeys is critical to improving their experience with your brand. Delving into the countless touchpoints that comprise a customer journey is complex enough, but skewing the process with bad data will prevent you from accurately addressing your users’ needs.

 

  • Ineffective marketing campaigns: Increasing ROI is an ongoing challenge for marketers. Biased data will only make that worse as you optimize and adjust based on flawed insights and incorrect assumptions.

 

  • Compliance violations: Compliance with GDPR and other privacy regulations is already a challenge, and AI can complicate the situation. One of the key components of the GDPR gives consumers specific rights about how their data is used for automated profiling or decision-making. If you have automated systems incorrectly profiling consumers due to data bias, you’ve opened yourself up to hefty fines.

 

  • Exacerbated organizational bottlenecks: Too often, marketing departments are at the mercy of IT or data scientists to provide information. When data bias creeps in, it adds extra work for data scientists to clean up, and extra delays for you to get the data you need.

How to prevent and mitigate data bias

Data bias is obviously 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:

 

 

  • Perform regular audits of your AI and ML algorithms. Are they performing as intended? Are you utilizing them in the best possible way?

 

  • Focus on complying with privacy regulations. Privacy laws grow stricter every year. Ensure that your systems are protecting your customers’ data, to protect you from hefty fines, and make sure you’re feeding clean, usable information into algorithms and analytical tools.

 

  • Separate the signal from the noise. Your customers will generate mountains of data, comprising billions of touchpoints, but not all of it is valuable. Determine what will be useful, then ignore the noise.

 

  • Keep your entire organization in alignment. Empowering marketing to work with data is great for organizational velocity, but don’t ignore the insight that data scientists can provide. Schedule regular inter-departmental meetings to make sure everyone has the resources they need, and that your data is being generated and handled as efficiently as possible.

 

  • Invest in real-time analytics. Waiting for a monthly report to discover that data bias skewed your massive marketing campaign can be a crippling mistake. Keep up with what’s happening in real-time so you can spot problems as they develop, fix them, and work on determining the root cause.

Why debiasing data matters

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 continuous intelligence platform can help:

 

  • Unify all of your brand's data so you can process it without missing anything important.
  • End-to-end privacy controls ensure your users' data is safe and secure.
  • Interactive, real-time dashboards provide constant insight into your customers' journeys.
  • Tools to perform complex queries with little technical knowledge.
  • Democratize data across your organization so that each department can focus on what they do best.
  • Easily keep customer data flowing into the system to ensure AI algorithms are always working with fresh, accurate data.

 

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.

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