Exploring the Gender Disparity in AI: How Men and Women Differ in Using ChatGPT and Other Tools

Defining the Gender Disparity in AI and Why It Matters

The phrase gender disparity in AI is shorthand for a set of related gaps: fewer women adopting or regularly using generative AI tools such as ChatGPT, differences in how women experience those tools when they do engage with them, and persistent underrepresentation of women in the teams that build models and shape product decisions. This is not an abstract academic curiosity. It affects productivity, fairness, and who benefits from the next wave of workplace automation—and, as businesses race to integrate generative AI, it creates both a commercial risk and a social equity problem. Deloitte’s analysis of women and generative AI frames these gaps as urgent: lower uptake erodes potential gains for organizations, while representation shortfalls contribute to products that don’t meet the needs of half the population. Echoing that concern, reporting shows fewer women adopting AI tools can matter materially for business outcomes.

Headline data are stark enough to focus attention: several surveys find women are roughly 20% less likely than men to use AI tools in everyday work; many workers have tried ChatGPT but only a minority use it regularly. This article explores those figures and asks why they matter. We synthesize public surveys and academic experiments, examine how trust and representation shape behavior, review evidence that large language models (LLMs) exhibit gendered patterns, and surface sector-specific risks—especially in finance, healthcare and education. We end with practical technical and organizational remedies and a roadmap for policymakers and researchers.

What follows combines data snapshots, summarized research findings, and real-world case studies so nontechnical readers—managers, policy makers, and curious professionals—can understand both the problem and what to do about it. Expect evidence-based explanations of the adoption gap, the role of trust and product design, examples of gendered outputs, and concrete actions firms can take to encourage women using ChatGPT and other generative AI more safely and confidently.

What we mean by gender disparity in AI

By gender disparity in AI I mean measurable differences in adoption rates and in qualitative experiences between men and women across three related domains: consumer-facing generative tools (for example, ChatGPT), workplace adoption and integration of AI into job tasks, and representation among developers and leaders creating these systems. Put simply, the gender gap in generative AI covers who uses the tools, how they are treated by the tools, and who decides how the tools are built.

Why this topic matters for business and society

When women are less likely to adopt or trust AI tools, businesses miss out on productivity and innovation gains; entire customer bases may receive poorly tuned guidance. More worrisome, bias in outputs can compound existing inequalities—biased financial advice can reinforce credit gaps, and skewed mental-health responses can lead to poorer support for women. Organizations that ignore women and generative AI risk losing market insight and creating harms that regulators and the public will eventually demand they fix. For that reason, discussions of women and generative AI are as much about competitive strategy as they are about fairness.

How this article uses evidence and case studies

This article draws on public surveys and media coverage for adoption statistics, academic preprints and experiments for evidence of model bias, and sector-specific experiments for real-world implications. When I cite studies, I link directly to primary sources so you can read the methods and limitations. Note that generative AI is a fast-moving field—adoption rates and model behavior can change quickly as new features and releases appear—so many findings are snapshots rather than permanent truths. Where possible I point out measurement caveats and suggest the types of long-term data (longitudinal panels, provider usage logs) that would give more reliable answers. Readers who want source material can follow the links placed throughout this piece to the original reports and papers.

Insight: The gender disparity in AI is not only a product problem or a user preference—it's the intersection of trust, design, and power.

The Scale of the Gender Gap in Generative AI Usage, ChatGPT and Beyond

The most commonly cited headline is simple to state: several analyses find women are about 20% less likely to use AI tools than men. That gap appears in consumer and workplace contexts and emerges across different surveys. For instance, reporting summarized by Tom’s Guide highlights this roughly 20% differential, while workplace research shows broad trial but limited habitual use: a study of U.S. workers found that about 57% had tried ChatGPT while 16% used it regularly, with clear demographic skews in who moved from trial to regular use, and patterns that favor men in ongoing integration into tasks. Those headline numbers set the scope of the issue: it’s not that women never try AI; it’s that they are less likely to make it a durable part of their workflows.

Key headline numbers and what they mean

The rough contours are these: studies report that women are about 20% less likely to adopt or experiment with AI tools; many employees—over half in some surveys—have tried ChatGPT, but only a minority use it habitually. For example, a workplace-focused survey summarized by Business.com found that while a majority of workers had at least experimented with ChatGPT, regular use clustered in certain roles and skewed male. Interpreting those numbers requires distinguishing trial from regular use: trying a tool once or twice is very different from integrating it into day-to-day tasks where the productivity gains (or harms) accumulate.

Key takeaway: Adoption gaps are not binary. The movement from “tried” to “regular use” is where disparities compound, and that transition is where product design, trust and workplace culture matter most.

Demographic and sector variations

The gender gap is not uniform across industries or roles. Tools that directly map onto traditionally male-dominated domains—certain engineering or software dev workflows—tend to show higher male adoption. Conversely, roles such as HR, customer service, or communications—areas where women are often better represented—may show more balanced uptake when product fit and training are in place. Age and education also confound the picture: younger workers and those with higher technical confidence adopt faster, and gender differences are sometimes smaller in cohorts with strong digital literacy programs.

An important nuance is job-task relevance: marketing and sales teams adopted generative copywriting tools quickly because the ROI is explicit; roles where AI’s benefits are less visible or where risk and privacy concerns are salient saw slower uptake. The phrase ChatGPT adoption by gender captures these workplace breakdowns: it’s not just who signs up for an account but who is encouraged, trained, and trusted to use the tool on real tasks.

Data limitations and measurement caveats

The measurement challenges are considerable. Survey wording—did the study ask about “tried,” “used in work,” or “used regularly”—changes outcomes. Cross-country differences in digital infrastructure and norms mean global averages can mask stark local variation. Most public data are cross-sectional snapshots; the field needs more longitudinal panels and anonymized usage logs tied to demographic data (with strong privacy safeguards) to understand trajectories. When discussing the gender disparity in AI, it’s useful to remember that reported gaps are contingent on who was surveyed, when, and how usage was defined.

Insight: Accurate measurement is a prerequisite for solutions—the more we can move from single-click surveys to sustained usage tracking (in privacy-preserving ways), the better we can target interventions.

Why Women Are Less Likely to Use ChatGPT and Other AI Tools: Trust, Representation and Perception

The question of “why” is complex because behavioral choices arise from multiple, interacting drivers. The leading explanations are trust and safety concerns, underrepresentation of women in product development and leadership, and perception of relevance. Each of these factors is visible in the public literature: Deloitte’s overview highlights trust and representation as central, and journalistic accounts such as Time’s coverage dig into the cultural and organizational levers that could raise adoption.

Trust and safety concerns that deter use

Trust in AI tools is a leading determinant of willingness to experiment and to rely on suggestions for substantive tasks. Women, on average, report more concerns over data privacy and safety, and less confidence in the accuracy of AI outputs—concerns that reduce experimentation and the integration of AI into workflows. Survey evidence suggests that perceived reliability and the risk of sharing personal or proprietary information play outsized roles in deterring sustained use.

Real-world implication: if an employee is worried that a prompt will leak customer data or produce misleading advice, they will avoid using the tool for decision-making even if it could save time. That avoidance, repeated across teams, reduces organizational learning and diminishes any productivity gains from the technology.

Representation in AI workforce and its effect on product design

Underrepresentation of women in technical and leadership roles shapes product design and priorities. When product teams do not reflect the full diversity of users, they are less likely to anticipate and test for the specific ways tools might fail for underrepresented groups. The result is weaker product fit and less advocacy for inclusivity features such as tone customization, explicit safety defaults, or privacy-preserving templates.

Hiring and leadership diversity—more women in the AI workforce—changes the questions asked during product development, from “how can we speed up drafting emails?” to “how can we ensure advice is equitable and sensitive to varied lifecircumstances?” Those questions matter because features and defaults steer user behavior.

Perceived bias and lived experiences shaping uptake

Perceived gender bias in AI shapes a feedback loop: published examples of biased outputs, or even anecdotes shared on social media, make women less likely to trust and test systems. That reduced experimentation means fewer critical use-cases surface to product teams, so models receive less corrective feedback where it matters. The loop—biased outputs reduce trust → lower adoption by affected groups → less corrective data and user feedback—can entrench disparities unless deliberately interrupted.

Insight: Trust is both psychological and structural. Fixing it requires technical corrections and visible social proof—stories and pilots that show the tool works equitably.

How ChatGPT and Large Language Models Exhibit Gender Bias, Evidence from Research

Large language models (LLMs) learn from vast text corpora that reflect societal patterns. That means models can reproduce stereotypes and default to masculinized language or framing unless developers intervene. A range of academic work documents these tendencies. For instance, experimental research and audits show gendered patterns across outputs; an arXiv paper examining gender bias in LLMs provides a methodological template for how researchers measure disparities, while a separate study on masculine defaults in AI discourse demonstrates how framing and language patterns can center male experiences even when prompts are gender-neutral.

Types of gender bias observed in LLM outputs

Gender bias in AI models appears in multiple, sometimes subtle, forms. Examples include:

• Stereotyping in career or financial advice: when prompts with neutral content elicit recommendations that assume traditional gender roles.

• Differential empathy or tone: mental health or caregiving prompts sometimes generate variations in warmth, agency, or validation depending on gendered cues.

• Masculine defaults in language: occupational examples, historical references, or default pronouns that center male experiences.

These biases arise through both direct mechanisms (training data that explicitly encodes stereotypes) and indirect mechanisms (model architectures and objective functions that magnify frequent patterns). Evidence that a tool is biased does not always mean it is maliciously designed; often it reflects unexamined patterns in the training corpus.

Key takeaway: Bias in ChatGPT and similar models is a systemic property of learned patterns. Fixing it requires deliberate intervention, not just hoping the model will generalize correctly.

Experimental methods that reveal bias

Researchers use several methods to surface gendered behavior: prompt-crafting (varying only the gender cue in otherwise identical prompts), matched comparisons (contrasting outputs for male- and female-coded profiles), and human rater evaluations that assess tone, accuracy, and fairness. These methods reveal statistical patterns rather than single-case proof; they show that across many prompts, systematic differences emerge.

Testing ChatGPT for gender bias typically involves controlled experiments where the only variable changed is the gender marker—this isolates model behavior from confounding factors. Human raters then score outputs for empathy, relevance, or stereotyping. While informative, these experiments have limits: they often rely on English-language benchmarks and curated prompts that may not capture every kind of lived harm.

Implications of masculine defaults and discourse framing

When models default to masculine frames—e.g., assuming male professionals in career examples or using male pronouns—female users can feel unseen or misrepresented. That experience matters: it shapes trust and the sense that the tool understands the user’s context. In domains like finance and health, masculine defaults can lead to less accurate or less relevant guidance for women, lowering the practical value of the tool for everyday decisions.

Insight: Models that center male examples are not just awkward—they structurally disadvantage users who do not fit those defaults, especially in critical domains.

Case Studies and Experiments Showing Gendered Outputs from ChatGPT and Tools

Empirical case studies make abstract concerns concrete. Selected experiments have documented gendered responses in finance, mental health, and clinical research settings—areas where biased outputs have measurable consequences.

Financial advice experiments and outcomes

An experiment summarized by the Center for Financial Inclusion tested ChatGPT by feeding it financial scenarios tied to male- and female-coded personas. The study found differences in tone and specificity: advice for female-coded personas was at times more risk-averse or framed in ways that assumed dependency, while male-coded personas received more agency-focused recommendations. The practical consequence is nontrivial: if automated advisors routinely suggest different risk profiles or investment approaches by gender, those patterns can amplify existing gaps in savings and entrepreneurship.

Keyworded as financial advice bias, these outcomes underscore how seemingly neutral prompts can yield gendered guidance that affects financial inclusion.

Mental health and education scenarios

Analyses of mental health prompts reveal variations in empathy, follow-up questions, and the seriousness with which concerns are treated depending on gender indicators. Reporting and experimental audits suggest that women-coded narratives sometimes receive different framing—either overly reassuring or insufficiently agentic—which can discourage follow-up help-seeking or produce suboptimal coping suggestions. Similarly, in education and career guidance, AI-generated suggestions have been shown to steer users toward stereotypical pathways when gender cues are present.

These findings—collected in journalism and targeted audits such as the sexism critique in BTW.media—are not uniform across every prompt but show enough consistent patterning to worry practitioners and researchers.

Clinical research and decision support usage differences

In research and clinical contexts, gendered outputs have implications for safety and reproducibility. A PubMed-indexed analysis of ChatGPT usage in clinical research settings highlights both adoption patterns and how model outputs can vary when prompts include gendered patient descriptions. While LLMs can speed literature reviews and draft reports, if they reflect gendered assumptions about symptoms, they may subtly influence diagnostic framing or provider recommendations. That risk elevates the need for domain-specific validation when models are used in healthcare workflows. See the PubMed discussion of ChatGPT in clinical research for more detail on adoption and implications (PubMed article on ChatGPT usage in clinical research).

Insight: Practical harms emerge when models are used as decision aids in contexts that materially affect people’s finances, health, or educational trajectories.

Impacts of the Gender Disparity in AI on Workplaces and Specific Sectors (Finance, Healthcare, Education)

When adoption and outputs are gender-skewed, the consequences ripple across sectors. The most obvious risks are in financial services, healthcare and education—domains where model outputs directly inform decisions. Beyond sectoral harms, workplace adoption gaps translate into competitive and equity impacts: teams that underuse AI miss efficiency gains; companies that build products with gender-blind assumptions risk alienating customers.

Financial services and access to quality advice

Financial inclusion experiments show that AI-generated advice can differ by gender-coded inputs. That matters because automated guidance is increasingly deployed in customer-facing applications—budgeting tools, loan counseling, or investment advice. If women receive more conservative or less tailored recommendations, the outputs can reinforce gender gaps in savings and entrepreneurship. The Center for Financial Inclusion’s experiment underlines this danger and suggests that careful prompt engineering and fairness tests are necessary before deploying automated advisors at scale.

Healthcare and mental health support

LLMs are increasingly used for triage, patient messaging, and mental health support tools. If these systems interpret symptoms or language differently according to gendered cues—or if they default to language patterns that minimize certain complaints—the downstream risk is mis-triage or suboptimal support. The stakes are regulatory and ethical: healthcare providers using these tools must ensure that outputs meet clinical standards and do not propagate gendered dismissals or biases. Labeling this problem as AI in healthcare gender bias helps anchor monitoring and audit strategies.

Education, career advice and long term opportunity costs

Educational guidance that nudges students toward stereotyped career pathways can have long-term effects on labor market representation. If girls and young women encounter AI tutors or career advisors that subtly frame STEM opportunities as less accessible or rewarding for them, the cumulative effect across cohorts can be significant. That dynamic feeds back into the talent pipeline and the representation in the AI workforce itself.

Key takeaway: Sectoral impacts are not theoretical—biased outputs and unequal adoption can worsen inequalities in finance, health, and education, with long-term implications for workforce composition and economic opportunity.

Technical and Organizational Solutions to Reduce Gender Bias and Encourage Inclusive Use

Solving these challenges requires parallel technical fixes and organizational commitments. Model builders can deploy debiasing techniques; product teams can redesign onboarding and UX; employers can run inclusive pilots and measure usage. The research literature on mitigation—from dataset curation to post-hoc calibration—provides actionable options, and Deloitte outlines organizational strategies to build trust and representation.

Technical approaches for model builders

Model-level interventions include dataset auditing to detect skewed representation, counterfactual data augmentation (adding alternative gendered examples to balance patterns), fairness-aware loss functions during training, and post-hoc calibration to correct biased output distributions. An accessible overview of these approaches appears in the technical literature on debiasing techniques for AI models (see an arXiv survey of debiasing methods). Developers should also evaluate models on differential performance across gender-coded prompts and make those metrics part of release gates.

These techniques are not silver bullets—some reduce one form of bias at the cost of another—but they form a necessary toolkit for builders who want to reduce systemic harms.

Product and UX strategies to improve inclusive uptake

Product teams can adopt design patterns that increase trust and relevance: explicit privacy defaults and templates for sensitive tasks, transparent notices about model limitations, and options to customize tone and level of detail. Onboarding flows that demonstrate concrete ROI for role-specific tasks—complete with side-by-side examples of safe prompts—reduce the psychological barrier to experimentation.

Building trust in AI tools also requires targeted testing with women and other underrepresented groups, using qualitative research to understand pain points and iterate on features. If the first experiences are small wins—time-savers on mundane tasks—users are more likely to expand usage into higher-stakes areas.

Organizational policies and workforce actions

Organizations should invest in representation in hiring and leadership, create continuous training opportunities for nontechnical staff, and measure AI usage by demographic groups to spot gaps. Practical measures include role-based pilot programs that intentionally include women-led cohorts, employee champions who can share success stories, and safe sandboxes where teams can test prompts without exposing sensitive data.

Leadership signals matter: when managers openly encourage responsible use and protect employees who experiment with AI—while enforcing clear governance—they create the cultural conditions for inclusive uptake.

Insight: Technical fixes without the organizational scaffolding will have limited impact. Adoption is both a product and a people problem.

Business Strategies to Encourage Women to Use ChatGPT and Generative AI at Work

For employers seeking practical ways to increase equitable adoption, the answer lies in role-specific interventions, privacy-first governance, and measurement. Forbes highlights the business case: inclusive adoption improves productivity and keeps companies attuned to broader customer needs. Practitioner perspectives—such as those shared on the podcast series Women With AI—reveal how peer learning and mentorship help normalize tool use.

Role-based pilots and hands-on workshops

Design pilots that show clear ROI for specific job functions, and purposefully recruit women-led cohorts to participate in and lead those pilots. Workshops should be hands-on, showing exact prompts, templates, and safety checks for tasks. A pilot that demonstrates “how this tool will save you two hours/week on report drafting” is more persuasive than abstract training. Encourage women to co-create prompt libraries and to provide feedback that product or centralized AI teams can act on.

Privacy, security and governance measures to build trust

Clear, communicated policies about data handling—what is logged, what is exposed to third-party models, and how sensitive information is redacted—reduce legitimate concerns. Offer opt-out choices for employees and supervised deployment for high-risk tasks. Publicly documenting governance steps builds internal trust and can also reassure external stakeholders.

Measuring success and iterating

Track adoption by gender and by role, measure outcomes (time saved, quality improvements), and collect satisfaction metrics to inform iteration. Use those metrics not as a compliance burden but as a learning tool: they reveal where prompts need better tuning, where training is missing, and where governance should be tightened. When adoption gaps narrow, capture and share the stories behind the change—peer testimonials are powerful.

Policy, Research and Future Directions for Closing the Gender Gap in AI

Closing the gender disparity in AI will require coordinated policy, funded research and public-private partnerships. The Financial Times and other outlets emphasize that policy levers—standards for testing and transparency—can accelerate improvements. Research priorities include longitudinal studies of adoption, intersectional analyses that consider race and socioeconomic status, and red-team evaluations that stress-test models for demographic harms. The arXiv work on masculine defaults underscores how linguistic patterns deserve regulatory attention when models act at scale.

Standards and public guidance

Industry guidelines for differential testing and transparency would help set expectations. Regulators could require demographic impact reporting for high-risk AI, making it routine to disclose how systems perform across gender and other groups. Such reporting would not solve bias, but it would create accountability and enable comparisons across vendors.

Research priorities and funding targets

Funders should prioritize experiments that test interventions to increase adoption among women (for example, targeted onboarding, privacy-first interfaces) and study downstream effects on outcomes such as earnings, health access, and educational choices. Support for tools that simplify safe AI use for nontechnical users—prompt wizards, privacy redaction modules, and role-specific templates—would lower the activation energy for broader uptake.

Monitoring progress and accountability

Public dashboards, independent audits, and third-party evaluations can track progress. As policies mature, stakeholders should insist on metrics that show both adoption trends and the direction of bias reduction. Monitoring should be continuous: models change, user practices change, and only ongoing evaluation can detect regressions.

FAQ: Common Questions About Gender Disparity in AI, ChatGPT Use, and Bias

• Is the gender gap in AI adoption measurable and meaningful? Yes. Multiple surveys show women are roughly 20% less likely to use AI tools, and workplace studies indicate broad trial but lower regular use among some demographic groups; see reporting that summarizes these patterns for context (Tom’s Guide on the 20% gap).

• Can ChatGPT outputs be biased against women? Yes. Experimental audits and academic papers document gendered patterns in outputs—ranging from masculine defaults to different tones in mental-health prompts—that can produce unfair or unhelpful guidance (see research into gender bias in LLMs and masculine defaults on arXiv: gender bias study, masculine defaults study).

• What immediate steps can an employee take to protect themselves from biased advice? Use prompts that include context (age, role, constraints), triangulate AI output with trusted sources, avoid using AI for sensitive personal or financial decisions without professional oversight, and follow privacy practices (do not paste sensitive personal data). For financial contexts, see the financial-inclusion experiment for examples of bias-aware prompting (Center for Financial Inclusion experiment).

• How should companies measure and report adoption differences by gender? Track both trial and regular use, disaggregate by role and department, and measure outcome metrics (time saved, quality improvement, satisfaction). Publicly documenting governance steps and measurement approaches builds internal trust and external credibility—start by following transparent workplace-study practices such as those summarized in the Business.com workplace study (Business.com ChatGPT workplace study).

• What are safe practices when using generative AI for health or financial advice? Treat AI-generated advice as a starting point, not a final decision. Validate recommendations with licensed professionals, use tools that are validated for clinical or financial use, and incorporate human oversight in any high-stakes workflow. See clinical adoption discussions for reasoning about how ChatGPT is used in clinical research contexts (PubMed on ChatGPT in clinical research).

• Who is responsible for fixing bias in ChatGPT and similar tools? Responsibility is shared: model builders must deploy technical mitigations and transparency; product teams must design for inclusive uptake; employers must govern safe use and measure adoption; policymakers should require disclosure and testing standards. Collective action reduces the likelihood of harms persisting.

Conclusion: Trends & Opportunities

Across the evidence—from surveys reporting a roughly 20% lower adoption among women, to workplace studies showing trial-but-not-regular-use patterns, to academic audits that document gendered outputs—the picture is clear enough to demand action and nuanced enough to resist simplistic fixes. The gender disparity in AI is simultaneously a measurement challenge, a trust problem and a design failure. It is a symptom of a broader social reality: systems trained on historically biased data will reflect those biases unless we deliberately counteract them.

On the horizon, expect a few dynamic trends. First, as firms deploy more in-house models and fine-tune on enterprise data, there is an opportunity to correct domain-specific biases—if organizations commit resources to evaluation and curation. Second, product UX that foregrounds privacy, role-specific templates, and tone-customization will lower barriers to entry for users who currently hesitate. Third, regulatory pressure for demographic impact reporting and third-party audits could accelerate transparency and make biases visible to the market.

Opportunities to act are concrete. AI creators should adopt debiasing techniques and differential testing as part of their release process; product teams should prioritize onboarding and safety features that address the concerns of underrepresented users; employers should run inclusive pilots that make benefits visible and measurable; and policymakers and funders should support longitudinal research and standard-setting. For women to feel comfortable and empowered using ChatGPT and other tools, they need respectful design, verifiable privacy safeguards, and visible proof that the technology works equitably.

There are trade-offs and uncertainties: some mitigation techniques affect utility; stricter governance may slow deployment; and adoption patterns will shift as generative models evolve. But the alternative—allowing an unaddressed bias-feedback loop to widen existing inequalities—carries its own costs.

If there is a single practical posture for leaders and practitioners, it is this: treat the gender disparity in AI as both an operational problem and a moral imperative. Measure it, design for it, and commit resources to closing it. Do that, and organizations not only reduce risk—they unlock broader innovation by making sure half the population has equal access to the productivity and creative potential of generative AI. For businesses and societies keen on inclusive technological progress, encouraging women to use ChatGPT and other generative AI is not optional—it is essential.