Joy Morales, M.A.
Founder and CEO, Women's AI Visibility Institute
Joy Morales is an educational researcher who studies how AI platforms like ChatGPT, Gemini, and Perplexity surface women attorneys.
About Joy Morales
Joy Morales is the founder and CEO of the Women's AI Visibility Institute (WAVI), a research organization that conducts empirical, state-by-state studies of how AI platforms surface women attorneys. Her work examines what happens when a potential client asks ChatGPT, Gemini, or Perplexity to recommend a lawyer: which attorneys those platforms name, how they describe them, and which sources they cite.
Morales brings more than 20 years of experience in education and research design to this question. Her central thesis, the Double Bias Problem, holds that AI systems compound two sources of bias: historical bias in the data they were trained on, and bias in the live web signals they draw from when answering questions. The result is that women attorneys are systematically underrepresented in AI-generated answers.
WAVI's research to date includes studies of 354 attorneys in Florida, 336 in Texas, 209 in Colorado, and the New York study of 435 attorneys with California in active data collection. The current AI Visibility Framework scores each attorney on a 112-point scale that measures how clearly their online presence communicates to AI systems.
Morales also led a 50-state census of Wikidata, a public database AI systems use to identify people and organizations. It found that only about 6.37% of documented attorney entities are women.
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This research has produced several concepts central to WAVI's work, including the Aggregator Substitution Effect, in which AI platforms cite directories and LinkedIn in place of an attorney's own website, and Platform Inversion, first identified in the Florida study.
Morales holds a B.A. in Communication from Michigan State University and an M.A. in Educational Research from Western Governors University. She is based in Colorado Springs, Colorado.
Published Research
Each study measures how ChatGPT, Gemini, and Perplexity describe and recommend women attorneys when a potential client asks about them by name.
New York Women Attorneys AI Visibility Report (September 2026)
435 attorneys across ChatGPT, Gemini, and Perplexity. The first WAVI study to include a comparison group of 243 men, making it the series' first direct test of whether gender itself shapes AI visibility.
[Read the Key Findings]
Florida Women Attorneys AI Visibility Report (Revised edition, September 2026)
354 attorneys matched across ChatGPT, Gemini, and Perplexity, scored on WAVI's 112-point framework. Found that 37.0% of attorneys fell below the visibility threshold on all three platforms, and introduced Platform Inversion: the platform with the most access to Google's database of known people and organizations was tied for recognizing these attorneys the least.
[Read the Key Findings]
Texas Women Attorneys AI Visibility Report (May 2026)
336 attorneys in Houston, Dallas Fort Worth, Austin, and San Antonio. WAVI's first three-platform study. It found that 84% of attorney websites carried behind-the-scenes code describing the site to search engines and AI, but only 28% labeled the business as a legal service, a pattern WAVI calls false presence.
[Read the Key Findings]
Colorado Women Attorneys AI Visibility Report (January 2026)
209 attorneys. WAVI's founding study, which established the baseline methodology for the series.
[Read the Key Findings]
50-State Wikidata Census
A national count of attorneys documented in Wikidata, a public database AI systems use to identify people. Only about 6.37% of documented attorney entities are women.
[Read the Key Findings]
Currently in Progress
California Women and Male Attorneys
Concepts and Frameworks
The following concepts were introduced by Joy Morales through WAVI's research to describe how AI platforms represent, and fail to represent, women attorneys.
The Double Bias Problem
WAVI's central thesis. AI systems carry forward bias in two layers: the historical data they learned from, and the live web sources they consult when answering a question. Each layer disadvantages women attorneys, and together they compound.
False Presence
A website that appears to be set up correctly for AI but is not. Many attorney websites include behind-the-scenes code that describes the business to search engines and AI, yet few label that business as a legal service. The code is there, but it tells AI the wrong thing. First documented in the Texas study.
Platform Inversion
When the AI platform with the greatest access to information about an attorney recognizes that attorney the least. First identified in the Florida study.
The Aggregator Substitution Effect
When AI platforms cite directories, review sites, and LinkedIn in place of an attorney's own website, letting third parties define who the attorney is and what she does.
The Perception Gap
The difference between what is actually on an attorney's website and what AI platforms report seeing there. A site can contain information AI does not recognize. First measured in the Florida study.
Entity Invisibility
When an attorney does not exist as a distinct, recognized individual in the databases AI systems rely on to identify people. Without that recognition, AI has no stable record to describe or recommend her.
Borrowed Visibility
When the visibility an attorney does have comes from someone else's platform, such as a directory, a firm page, or a social network, rather than from sources she owns and controls. WAVI's response is a four-part framework: Infrastructure, Authorship, Identity, and Attribution.
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Books and Articles
Books and Publications
Joy Morales writes at the intersection of artificial intelligence, professional visibility, and gender equity. Her work draws directly on the Women's AI Visibility Institute's state-by-state empirical research into how ChatGPT, Gemini, and Perplexity recognize and recommend women attorneys.
The WAVI eBook Series
The Double Bias: Why AI Can't Find You, and Why It's Not Your Fault (Book One)
Introduces the Double Bias, WAVI's central finding. The book explains how a technical gap in attorney websites combines with a systemic gap in the public record AI learns from, and why women attorneys fall behind when both are present. Drawing on WAVI's Colorado and Texas studies, it maps the problem and the four-level ladder for addressing it.
The Visibility Playbook: A Woman Attorney's Guide to Taking Control of How AI Sees You (Book Two)
The implementation companion to The Double Bias. Readers run their own three-platform visibility check, then work through each layer of AI visibility: how their website identifies them to machines, the consistency of their professional profiles, their presence in the public knowledge sources AI relies on, and the body of third-party evidence AI uses to decide whom to recommend.
Rewriting the Record: How Women Attorneys Can Reclaim Their Place in the AI Future (Book Three)
The structural companion to the series. It addresses what individual action cannot fix alone, and what bar associations, law firms, and professional organizations can do together. Topics include the Wikipedia gender gap as an AI problem, the edit-a-thon model for collective action, and the business case for firm-level investment in women attorneys' visibility.
Research Reports
Colorado Women Attorneys AI Visibility Report (January 2026)
WAVI's inaugural study, covering 209 women attorneys. It was the first study to measure AI visibility across a full state population of women attorneys.
Texas Women Attorneys AI Visibility Report (2026)
The first three-platform study in the series, covering 336 attorneys across Houston, Dallas, Austin, and San Antonio. It documented the false presence problem: most attorney websites carry structured data, but few use the designation that identifies them as legal professionals.
Florida Women Attorneys AI Visibility Report (Revised Edition)
A matched three-platform analysis of 354 attorneys. It introduced Platform Inversion, Entity Invisibility, the Perception Gap, and the Aggregator Substitution Effect.
New York Women Attorneys AI Visibility Report (September 2026)
A matched three-platform analysis of 435 attorneys. It is the largest study in the series to date, and the first to include a male comparison group.
Article Series (published on LinkedIn)
The WAVI Research Series
A five-part series that builds WAVI's research into a single framework. It moves from why AI recommends individuals rather than firms, through the three-state pattern of absence, false presence, and entity invisibility, to the career cost of visibility attorneys do not own.
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Why AI Recommends People, Not Firms
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The Double Bias: Why Women Attorneys Start From Behind on AI Platforms
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False Presence: What 336 Texas Attorneys Taught Us About Schema Markup
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The Three-State Pattern: Absence, False Presence, and Entity Invisibility
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Borrowed Visibility: Why Women Attorneys Start From Zero Every Time They Move
The Four-Platform Series
A five-part series on how AI answer engines decide whom to cite. It argues that the platforms are not separate universes but separate channels drawing from one shared foundation: a well-built, verifiable professional identity.
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One Entity Hub, Four Pipes
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The Hub
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The Door, Not the Room
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Retrieved, Not Chosen
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Two Gates, One Citation
Selected Article
Won Before the Phone Rings: How Attorneys Actually Get Hired, and the Invisible Stage Where Women Lose First
Traces the four stages that come before a prospective client's first call. It shows why the two visibility stages, being surfaced and being verified, are where women attorneys are disadvantaged first.
Forthcoming
The Agent Layer (white paper)
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