Demographic bias in data occurs when datasets don’t include information from a broad, diverse group of subjects. For example, a company might collect information from 100 people, 90 of whom identify as male and 10 as female. The over-representation of male opinions could skew the dataset significantly, often in favor of the majority at the minority’s cost.
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Real-Life Examples of Demographic Bias
Several types of biases can tarnish datasets and the conclusions people draw from them. Common biases include confirmation bias, historical bias, and selection bias.
With demographic bias, you lack the critical information needed to draw accurate conclusions. Demographic bias can also interact with other types of biases. For example, selection bias that influences who gets to participate in a survey can contribute to demographic bias, especially when the survey goes to people with certain characteristics.
Demographic bias can occur when collecting any type of information. For example, facial recognition software primarily trained with images of white, cisgender men struggles to identify people who fall outside of those demographics. While Amazon’s facial recognition software accurately identifies 95% of cisgender men, it often fails to identify transgender people. Similarly, demographic bias makes it difficult or impossible for the software to identify people of color. A face-detection API from Amazon Web Services (AWS) even makes more errors when trying to identify older users.
Amazon isn’t alone. Similar flaws exist in facial recognition software used by Microsoft and Google.
The Effects of Demographic Bias in Data
Demographic bias in data presents several troubling issues for companies that want to engage with as many consumers as possible. An incomplete dataset used to guide a marketing campaign might primarily appeal to a smaller number of people, making it difficult for companies to reach a broad range of people and sell more products.
The negative effects of demographic bias in data can go far beyond the business world, though. Biased research can create barriers that make it harder for some people to use new technologies. If facial recognition software fails to identify users with dark skin, it could block them from accessing the technologies others find easy to use.
Demographic bias can also prevent access to effective healthcare. If researchers rely on data primarily drawn from a specific demographic, they might only develop treatments that work for people in that demographic. Anyone outside of the group gets left behind, while those within the group get treatments that improve their lives.
The growing importance of artificial intelligence (AI) highlights some of the most troubling effects of demographic bias. The topic is so important that the United Kingdom’s Department for Science, Innovation & Technology published a report to help developers understand demographic bias and explore novel approaches to preventing it. Failing to address these topics could mean that sophisticated AI products don’t work well for marginalized groups. Misinformed artificial intelligence might also encourage government and business leaders to adopt harmful policies that ostracize entire segments of society.
All businesses need to reduce demographic bias as much as possible so their algorithms make accurate predictions. Become a more effective data analyst by registering for the Business-Driven Data Analysis course available from Pragmatic Institute.
Preventing Demographic Bias in Data
It will take a lot of work to prevent demographic bias in data. As the U.S. Department of Commerce explains, statistical biases are more than technical issues. They exist because of human and systemic biases.
What can business leaders do to prevent – or at least curb – demographic bias within their organizations? It likely helps to acknowledge that biases exist and start looking for ways to improve data quality.
Specific strategies might include reviewing datasets to ensure they include information from diverse groups, getting more minorities involved in data collection and analysis, and addressing other forms of bias that might interfere with accuracy.
Demographic bias in data could negatively affect several aspects of a business, including its marketing, product development, and testing. It can also contribute to social barriers that prevent more people from enjoying products and services. Register to take Business-Driven Data Analysis from Pragmatic Institute so you can learn how to improve data and reduce the potential impact of demographic bias.