Health Gender Bias: Platform & Algorithmic Visibility Bias (Digital Health)

Initiative: Health Information Suppression Index

What this is

The Health Information Suppression Index is a system that audits how digital platforms treat women’s health information—not at the level of content quality, but at the level of algorithmic visibility and access.

It asks a structural question most health equity efforts ignore:

Is women’s health information being made harder to find, share, or fund by the platforms that mediate modern health knowledge?

The answer, consistently, is yes—and the bias is encoded in algorithms, policies, and moderation systems rather than explicit rules.

The core problem

In digital health ecosystems:

  • search engines decide what information surfaces first

  • social platforms decide what spreads

  • ad systems decide what can be promoted

  • moderation tools decide what is “allowed”

Women’s health topics—especially those involving reproduction, pain, hormones, or sexuality—are disproportionately:

  • labeled as “sensitive”

  • restricted in advertising

  • downranked in search

  • shadow-banned or demonetized

  • blocked by automated moderation

This creates an information gap before a patient ever reaches a clinician.

AI approach: auditing visibility as infrastructure

1) Cross-platform crawling

The system systematically crawls:

  • search engine result pages

  • social platform feeds and recommendations

  • advertising approval and rejection flows

It does this using controlled, repeatable queries covering:

  • women’s health conditions

  • equivalent men’s health conditions

  • neutral medical controls

This allows direct comparison of how platforms treat medically analogous content by gender association.

2) Visibility and suppression measurement

For each platform and query set, the system measures:

  • approval vs rejection rates (especially for ads and promoted content)

  • ranking position and result depth

  • impression throttling and reach limits

  • keyword blocking and substitution

  • unexplained content removal or de-prioritization

Importantly, the system distinguishes policy-based restriction from algorithmic suppression, which is often undocumented.

3) Keyword-level bias analysis

Using NLP and counterfactual testing, the system evaluates:

  • which women’s health keywords trigger suppression

  • how euphemisms or clinical phrasing alter visibility

  • whether equivalent male-associated terms are treated differently

For example, “erectile dysfunction” vs “menstrual pain” may receive radically different algorithmic treatment despite similar clinical relevance.

What the system detects

A) Structural visibility gaps

Where women’s health information:

  • appears lower in search rankings

  • is less likely to be recommended

  • requires more precise phrasing to surface

  • is excluded from monetization pathways

B) Sensitivity misclassification

Patterns where:

  • routine women’s health topics are flagged as sexual, graphic, or political

  • reproductive or hormonal content is treated as inherently risky

  • educational material is moderated as if it were advocacy or adult content

C) Information access inequality

Situations where:

  • patients searching for women’s health topics encounter less authoritative content

  • misinformation fills the vacuum left by suppressed credible sources

  • health creators self-censor to avoid penalties

Core outputs

1) Platform-level bias scores

Each platform receives a composite score reflecting:

  • relative visibility of women’s vs men’s health content

  • approval disparities

  • suppression frequency

  • transparency gaps in moderation decisions

These scores enable comparison, accountability, and longitudinal tracking.

2) Evidence packages for regulators and advocates

The index produces audit-ready outputs:

  • reproducible query logs

  • before/after ranking comparisons

  • statistically significant suppression differentials

This transforms anecdotal claims of “shadowbanning” into regulatory-grade evidence.

3) Early warning signals for information deserts

By tracking changes over time, the system identifies:

  • sudden drops in visibility for certain topics

  • policy shifts that disproportionately affect women’s health

  • emerging areas where credible information is being algorithmically starved

Bias exposed (in systemic terms)

The index makes clear that:

  • women’s health is algorithmically framed as controversial or risky

  • male-associated health topics are treated as neutral and informational

  • platform “safety” policies encode cultural discomfort, not medical necessity

  • access to health knowledge is being shaped by non-clinical values

In short:

Women’s health is treated as sensitive content.
Men’s health is treated as essential information.

Why this matters for health outcomes

When visibility is suppressed:

  • patients delay seeking care

  • misinformation fills gaps

  • clinicians face less-informed patients

  • trust in institutions erodes

When visibility is equitable:

  • early symptom recognition improves

  • evidence-based resources reach those who need them

  • public health messaging becomes effective

  • digital platforms become health infrastructure, not gatekeepers

The larger shift

The Health Information Suppression Index reframes digital health equity from:

“Is content allowed?”
to
“Is content discoverable, amplifiable, and treated as legitimate?”

Because in a digital-first world,
what cannot be seen might as well not exist—and algorithmic invisibility is itself a form of structural bias.

Health Gender BiasFrancesca Tabor