Equal Treatment Ads Can Backfire and New Research Shows Why Fairness Rules May Actually Boost Ad Revenues

Online advertising platforms are under growing pressure to make sure their systems treat everyone fairly, especially when it comes to ads for jobs, education, housing, and financial opportunities. These are often called economic-opportunity ads, and they matter because they shape who even gets a chance to apply for a job, enroll in a course, or access financial products. A new academic study, however, suggests that some of the most common fairness policies used today may not work the way people expect. In fact, equal treatment alone can backfire, while a more carefully designed fairness rule can improve both equity and profits.

The study, published in the peer-reviewed journal Marketing Science, is titled Is Fair Advertising Good for Platforms?. It was conducted by Di Yuan of Auburn University, Manmohan Aseri of the University of Maryland, and Tridas Mukhopadhyay of Carnegie Mellon University. The researchers set out to answer a simple but critical question: do fairness-focused advertising rules hurt or help online ad platforms financially, and do they actually improve outcomes for protected groups?

Why Economic-Opportunity Ads Are Different

Not all ads are created equal. Consumer advertising, such as ads for clothing, electronics, or cosmetics, often values certain demographic groups more than others. For example, women are considered a highly desirable audience for many consumer brands, which means advertisers are willing to bid more money to reach them.

Economic-opportunity advertisers work differently. Employers, universities, or lenders usually want to reach everyone equally, regardless of gender or race. A job opening or a degree program is not inherently more valuable when shown to one group over another. The problem is that these advertisers must still compete in the same ad auctions as consumer brands.

This creates what the researchers call an asymmetric valuation problem. Consumer advertisers aggressively bid to reach certain demographics, while economic-opportunity advertisers cannot justify bidding as high for those same users. As a result, women and other protected groups often end up seeing fewer job, housing, or education ads online, even when there is no intentional discrimination.

The Three Advertising Policies Studied

To understand how platform rules affect this imbalance, the researchers modeled advertiser competition under three different policy regimes.

The first is No Restriction (NR). Under this approach, advertisers can freely target ads based on demographics such as gender or age. There are no fairness constraints imposed by the platform.

The second policy is Equal Treatment (ET). This is one of the most widely adopted rules today, largely due to regulatory pressure. Under equal treatment, economic-opportunity advertisers are not allowed to target ads by demographic group. The idea is that by treating all users the same, platforms can prevent discriminatory outcomes.

The third and most interesting policy is Equal Exposure with Equal Treatment (EET). This approach goes a step further. Not only are demographic targeting options restricted, but platforms also actively ensure equal per-capita exposure to economic-opportunity ads across demographic groups. If needed, platforms adjust how ad budgets are effectively allocated to maintain that balance.

Why Equal Treatment Alone Often Fails

One of the most surprising findings of the study is that equal treatment by itself does not solve the exposure problem. In some cases, it actually performs worse than having no restrictions at all.

When demographic targeting is removed but exposure is not actively managed, the underlying competitive dynamics remain unchanged. Consumer advertisers still outbid opportunity advertisers for certain audiences, and the exposure gap persists. In some scenarios, platform profits even decline under equal treatment rules, because advertiser competition becomes less efficient.

In short, treating everyone the same does not guarantee fair outcomes. The system still favors advertisers with higher willingness to pay for specific user groups, and economic-opportunity ads continue to get crowded out.

How Equal Exposure Changes Everything

The study finds a very different outcome under the Equal Exposure with Equal Treatment policy. When platforms commit to ensuring that all demographic groups receive similar levels of exposure to economic-opportunity ads, advertiser behavior changes in meaningful ways.

The key insight is that the existence of the rule itself reshapes competition. Advertisers can no longer avoid competing with each other by focusing on different demographic segments. This reduces market segmentation and pushes advertisers into more direct competition.

As competition intensifies, advertisers collectively spend more on ads. That increased spending leads to higher total ad revenues for platforms, even though fairness constraints are in place. At the same time, protected groups such as women and minorities see a measurable increase in exposure to job, education, and financial opportunity ads.

This is one of the rare cases where fairness and profit goals align instead of conflicting.

What This Means for Ad Platforms

The findings challenge a long-standing assumption in the tech industry: that fairness policies necessarily come at the cost of revenue. According to this research, the problem is not fairness itself, but how fairness is implemented.

Policies that focus only on equal treatment, without considering exposure and competition dynamics, can fail both socially and financially. On the other hand, carefully designed rules that balance exposure can lead to better outcomes for users and platforms alike.

For major ad platforms that are constantly navigating regulatory scrutiny, public trust, and revenue goals, this research offers a compelling data-driven argument for rethinking current approaches.

Broader Context: Fairness in Algorithmic Advertising

This study fits into a much larger conversation about algorithmic fairness. Over the past decade, researchers and regulators have documented how automated ad systems can unintentionally reinforce social inequalities. Even without explicit bias, optimization for clicks, conversions, or revenue can produce skewed outcomes.

Economic-opportunity ads are especially sensitive because they directly influence access to life-changing resources. When certain groups systematically see fewer of these ads, inequality can deepen quietly and invisibly.

What this research shows is that fixing these issues requires more than surface-level rules. Platforms need to understand the economic incentives baked into their systems and adjust them in ways that encourage healthy competition rather than avoidance.

Why This Research Matters Going Forward

As governments around the world consider stricter regulations on digital advertising, evidence-based insights like these are crucial. Instead of relying on intuition or political pressure alone, platforms can use rigorous modeling to design policies that actually work.

The takeaway is not that fairness is bad for business. Rather, poorly designed fairness rules are bad for both fairness and business. With the right structure, platforms can support equal access to opportunity while maintaining, or even increasing, their revenues.

For anyone interested in digital advertising, platform economics, or tech policy, this study offers a rare and encouraging message: with smart design, doing the right thing can also be the profitable thing.

Research paper: https://pubsonline.informs.org/doi/10.1287/mksc.2023.0201