Bias in advertising amplifies societal stereotypes. These skewed representations not only limit the narrative scope we collectively share but also shape public perceptions of individuals’ roles and capabilities. As advertisers, it’s our responsibility to challenge these norms and craft more inclusive and diverse campaigns.
This article examines the different types of biases in advertising, how to identify bias in your advertising efforts, and reviews biased advertising examples from major brands. Our aim is to provide actionable insights for brands striving for authentic and unbiased campaign representation.
Bias in advertising refers to the unequal or unfair representation, subjective opinions, or one-sided perspectives within ads. It relies on assumptions about groups of people rather than objective truth.
Bias can be both intentional (overt), where advertisers purposefully push a particular perspective, or unintentional (covert), stemming from ingrained societal norms and prejudices.
The most common biases are based on characteristics such as:
Bias Ad Example: The “More Doctors Smoke Camels” Campaign (1946)
This biased ad example exploits the public’s trust in doctors to promote cigarette smoking while downplaying smoking’s health risks.
Bias in advertising can influence consumer perceptions, behaviors, and societal attitudes, making it a crucial concern for ethical advertising practices.
Biased advertising takes shape through various means, including stereotyping, underrepresentation, misrepresentation, and/or appeal to authority.
In the following section, we’ll delve into each of these types, highlighting biased advertising examples.
Unconscious biases, or implicit biases, are deeply rooted assumptions about certain groups. Formed by upbringing, culture, and societal influences, they subconsciously influence our decisions and interactions, often going unnoticed.
Examples of Unconscious/Implicit Bias
In advertising, racial, gender, and age biases are the most common types, manifesting in many overlapping ways:
Below, we’ll review each in more detail with examples.
Racial bias refers to the subconscious prejudices and stereotypes regarding individuals of a different race.
One of the most prevalent forms of racial bias in advertising is underrepresenting racial and ethnic minorities. For years, advertisements primarily featured individuals from dominant racial groups, often sidelining minority voices and faces.
This ad harmfully depicts a white woman dominantly positioned over a black woman, suggesting superiority. Portrayals like this can perpetuate harmful racial stereotypes by implying that white is preferable and dominant. Sony later acknowledged the ad’s potential racial insensitivity and apologized.
Gender bias stems from preconceived notions and stereotypes associated with a person’s gender. It often leads to the belief that one gender is better suited for specific roles or tasks.
For instance, research from Zipdo found that 25% of commercials depict males as the family’s primary breadwinners. Additionally, a study by Unilever discovered that only 3% of ads feature women in leadership roles.
Biased Advertising Laws in Other Countries
In 2019, the UK’s Advertising Standards Authority banned ads showcasing gender stereotypes, such as women solely cleaning or clueless men with diapers. Brooke Erin Duffy, a Cornell communication professor, noted that the UK’s standards exceed the U.S.’s. However, other countries have previously implemented laws against gender discrimination in ads.
Pine O Cleen is an Australian disinfectant supplies company. This was one of their many ads promoting their cleaning wipes product.
The ad, showing a woman relaxing alongside a cleaning product, suggests that cleaning is mainly a woman’s duty and that using Pine O Cleen wipes can help them save time for leisure.
Ageism pertains to prejudices or discrimination against individuals based on their age. Often, older adults are the primary victims, depicted in ways that either make light of their capabilities or paint them in an unflattering light.
People 55+ now control 70% of all personal wealth in the United States, but advertising is too often out of sync with today’s aging majority buyer population.
The negative impact is multiplied for older minorities.
The “Dear Young People, Don’t Vote” ad used satire to urge youth participation in the 2018 U.S. midterms. Featuring older individuals mockingly advising youth not to vote, it emphasized the impact of young voters’ choices and motivated them to have a say in key issues.
This glaring ad, backed by the D.C.-based nonprofit Acronym, perpetuates ageist stereotypes, portraying older individuals as universally apathetic and out-of-touch with the concerns of younger generations.
Ageist ads portray elderly individuals as caricatures. Respectful ads portray older adults as part of the community, as mentors and friends.
Statistical bias refers to data distortion that results in misleading conclusions. This typically occurs when the data collected doesn’t truly represent the entire population it’s meant to describe.
Data, especially from companies lacking data maturity, sometimes unintentionally favors one group over another due to various data collection constraints like using unclean Third-Party data without supplementing with First-Party data directly from a company’s own customers.
A retail company relied solely on Third-Party data to determine market demand for a new product. However, because they didn’t supplement with First-Party data from their existing customer base, they overlooked key preferences unique to their audience. This resulted in a product misalignment and lower overall sales.
The company made decisions based on external data that may not have represented its specific customer base. Not including their own First-Party data introduced a potential skewness, failing to account for their audience’s needs and wants.
Advertising increasingly leans on platforms that segment audiences, tailor offers, and fine-tune creatives automatically. It’s important to recognize that this isn’t inherently neutral.
Technological bias arises when human prejudices and stereotypes happen because of the data sets engineers use to train the platforms. If the data sets contain biased information, the output can be prejudiced.
Facial recognition algorithms have higher error rates for darker-skinned females.
Face recognition algorithms can show biased accuracy rates across demographics. The “Gender Shades” project in 2018 found that algorithms, including those from IBM and Microsoft, had higher error rates for darker-skinned females, sometimes 34% higher than lighter-skinned males.
This disparity reveals underlying dataset prejudice, leading to technological systems that don’t serve all users equally.
Systemic bias, often called institutional bias, is ingrained in the structures of organizations, policies, or societal norms. It’s not about individual prejudices but rather about overarching practices that result in discriminatory outcomes for certain groups.
Here are some ways systemic bias occurs in advertising:
Of course, there are products often tailored for specific demographics, like maternity wear for pregnant women or certain hair products for specific hair types.
While targeting ads for these products to relevant groups is strategic marketing, systemic bias arises when stereotypes dictate ad practices, leading to exclusion or misrepresentation. The goal is to balance genuine needs with avoiding harmful stereotypes.
Example: Dove Soap
In 2017, Dove released an ad where a Black woman transformed into a white woman, evoking a long-standing racist trope in soap advertising: a “dirty” black woman cleansed into whiteness.
Dove’s ad unintentionally perpetuated racial stereotypes equating Blackness with impurity. Its production and approval highlight ingrained biases within the advertising industry’s processes.
3. Cognitive Biases
Cognitive biases are systematic patterns of deviation from norm or rationality in judgment. These biases can lead individuals to make illogical conclusions or decisions that aren’t in their best interest.
There are over 180 cognitive biases, so we can’t list them all, but below are the most common types in advertising.
Confirmation bias is the tendency to favor information that confirms one’s existing beliefs and dismiss contradictory data. In other words, people are more likely to accept information that aligns with their existing views and reject or overlook information that contradicts them.
A brand primarily gathers and emphasizes positive feedback or reviews to reinforce its image, while disregarding or downplaying negative feedback that contradicts its desired image.
Focusing on only positive reviews can mislead consumers, affecting their purchases, and erode trust in the brand if the product doesn’t meet expectations.
Exposure bias, also called the “mere exposure effect”, is when individuals prefer things simply because they’re familiar with them. The more we’re exposed to a particular stimulus, the more likely we will view it favorably.
Many brands exploit this bias to work to their advantage with strategies like retargeting. A study found that retargeted campaigns generated 10x higher click-through rates and 70% greater conversion rates.
Exposure bias isn’t inherently bad; it can foster brand loyalty but might also reduce openness to new ideas and narrow one’s media worldview by favoring familiar sources.
Coca-Cola frequently places its advertisements in high-traffic areas and during prime-time television slots, ensuring that consumers are repeatedly exposed to the brand.
As a result, many people may choose Coca-Cola over lesser-known sodas simply because of their familiarity with it, demonstrating exposure bias.
Despite best efforts, bias can subtly infiltrate even the most well-intentioned campaigns. Historically, advertisers used simplistic methods to target demographics, leading to inaccurate and ineffective assumptions based on specific websites and ethnicities.
It’s tempting to generalize, thinking that people of a certain ethnicity predominantly visit particular websites, but this is a crude and outdated strategy.
Here are 5 tips to avoid bias in your advertising campaigns:
1. Use a Data-Driven Approach
The advertising bias comes from assumptions marketers make when targeting their campaigns. The key is to use data over broad assumptions about where that audience spends time online. What is the data telling you?
By leaning heavily into quantitative data like First-Party, advertisers can understand real user behaviors rather than relying on preconceived ideas.
Tools like KORTX’s Axon Audience Manager give you real insights about your target audience, driving successful campaigns without making assumptions.
2. Define Your Goals
Before launching any advertising campaign, define and understand your specific goal. Is the primary aim to increase product sales or raise awareness for a particular event like Juneteenth or a cause?
When targeting a specific demographic, it’s imperative that your messaging resonates authentically. This requires thorough research into what messages are most effective for that audience.
For instance, if your aim to to engage with a particular ethnicity, ensure that your content speaks directly to their values, preferences, and cultural nuances.
Rely on data sources that reflect self-declared information. Some platforms, like dating sites, allow users to specify their ethnicity, providing a more reliable data source than probabilistic models.
Probabilistic models make assumptions about an individual’s characteristics or behaviors based on patterns observed in data.
Companies often find themselves walking a fine line between genuine representation and pandering in their ads.
To tread this line:
As long as you’re using data to inform your campaigns and not relying solely on assumptions or stereotypes, you’ll be better positioned to create authentic, impactful advertisements that genuinely resonate with your target audience and uphold the integrity of your brand.
Kate Meda is a Copywriter at KORTX. She enjoys omitting needless words and making things sound good.
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