Don’t let AI decide for you: 10 ways women can take control

Interface reports that roughly 29 % of AI‑skilled workers are women, leaving a 42 percentage‑point gender gap in the field.

This disparity reflects a broader pattern in STEM and tech sectors, where women remain underrepresented at technical and leadership levels despite gains in overall workforce participation.

Understanding how AI reflects and sometimes magnifies these gaps is the first step toward reclaiming agency and ensuring that technology serves as a tool for empowerment rather than a shortcut to abdication.

Be Aware That AI Doesn’t Eliminate Bias

Artificial intelligence systems are trained on historical data and therefore inherit the patterns, including discrimination, that exist in that data. In early 2026, an internal audit of a major U.S. tech company’s AI resume‑screening tool found that women were rejected more than three times as often as men for developer roles, not because of talent but because the system had learned from a decade of male‑dominated hiring data.

Researchers such as Dr. Joy Buolamwini have shown parallel bias in facial recognition systems, where error rates for darker‑skinned women were far higher than for lighter‑skinned men, exposing how training data shapes real outcomes. Buolamwini reminds us that, “Default settings are not neutral. They reflect the coded gaze, the preferences of those who choose what the system focuses on.”

In the U.S., these patterns play out not just in tech recruitment but in sectors like lending and healthcare, where AI influences credit decisions and medical diagnostics. The danger is not a malicious machine; it’s a mirror of societal inequities dressed in confident language and automation. For women, especially women of color, this means AI can appear to decide for them when in fact it is echoing unresolved social disparities.

Know That AI Can Impact Access to Jobs and Careers

Automated hiring technologies are increasingly used across the United States to filter applicants and evaluate talent at scale. Research from Brookings and other institutions shows that AI resume‑screening tools can disproportionately favor male‑associated names and profiles over female ones, even when qualifications are comparable. In another documented pattern, algorithms trained on historical engagement data have reinforced gendered job ad targeting, showing technical roles more often to men and lower‑wage opportunities to women.

A 2025 Marie Claire analysis noted that women are often overrepresented in entry‑level and administrative positions that are most vulnerable to automation, raising real concerns about economic security. Rather than passive acceptance, women can stay in control by asking employers how AI is used in recruitment, what fairness checks are in place, and whether human review accompanies automated decisions.

Knowledge is power: understanding where AI intersects with hiring means women can press for equitable processes, not just compliant ones. Without that awareness, AI’s efficiency can mask ongoing inequality in workplace opportunity.

Protect Your Financial and Credit Decisions from Black‑Box Defaults

AI is also used in financial services for credit scoring, lending decisions, risk assessment, and loan pricing, areas that have profound impacts on women’s economic agency. Historical patterns of bias in lending mean that data‑driven systems can replicate those inequalities unless explicitly corrected, with women often scoring lower on risk metrics despite responsible financial behavior.

Without transparency into how these systems weigh factors, individuals have little insight into why an application was accepted or rejected. Some states and advocacy groups are pushing for clearer disclosures and auditing requirements to ensure that automated credit decisions comply with anti‑discrimination law.

Staying in control means asking lenders whether they use algorithmic scoring and demanding an explanation if your application is denied. A refusal to accept a coded “no” without justification turns a passive process into an actionable demand for accountability. Women who understand the contours of these systems can better navigate them and challenge unfair outcomes.

Build AI Literacy as a Form of Power, Not Deference

One of the biggest risks isn’t that AI will “take over,” but that people will begin to treat it as an unquestionable authority.

47 % of U.S. adults say AI would do a better job than humans at treating all job applicants the same, compared with only 15 % who think it would do worse. This shows that many Americans assume that automated systems must be fairer or more objective than humans , even when they aren’t. This misplaced confidence can lead users to accept AI output without verification or reflection. For women, cultivating a deeper understanding of what AI can and cannot do, from the limitations of probabilistic predictions to the role of training data, turns passive consumption into empowered engagement.

Workshops, community tech education, and professional courses on ethical technology are ways to increase this fluency. Literacy reduces the allure of blind trust and instead positions AI as a tool to interpret, not a voice to obey. When women know the mechanics and boundaries of AI, they are far less likely to defer responsibility to it.

Ask Clarifying Questions Before Accepting Automated Suggestions

Humans naturally probe uncertainty by asking questions, refining requirements, and seeking confirmation.

AI systems, however, tend to generate outputs based on defaults and statistical patterns without hesitation, even when underlying objectives are vague. That means an AI may produce an answer that seems plausible without actually addressing what you need. Before acting on any AI recommendation, whether it’s career advice, legal wording, or health guidance, define success criteria and ask the tool to clarify ambiguous points.

If this feels like what you would do with a human collaborator, it’s a good prompt to use with AI as well. Demanding precision from the outset forces the system to generate outputs that align with your priorities, not generic interpretations. Accepting smooth output without scrutiny makes it easy to abdicate responsibility without realizing it.

Champion Transparency and Accountability in AI Deployment

In the U.S., there is growing scrutiny of algorithmic decision‑making in workplaces and public services. Mobley v. Workday, Inc.:  a class/collective action lawsuit filed in February 2023 in the U.S. District Court for the Northern District of California, alleges that Workday’s automated resume‑screening tools discriminate against applicants based on protected characteristics like race, age, and disability.

States like California- SB 53, Colorado AI Act (SB 24‑205), New York City’s Local Law 144, and Illinois- House Bill 3773 have introduced regulations requiring employers and vendors to audit and document the fairness protocols of their AI systems. In these regulatory environments, women and advocates can insist that any AI‑assisted decision come with explainability: a human‑readable rationale for how the automated recommendation was reached.

Accountability isn’t guaranteed by technology. It’s enforced by law, governance, and by persistent user demand that refuses to accept “algorithm says so” as a final answer. When women exercise their rights to transparency and redress, they reinforce their agency and set precedents for equitable practice.

Support Inclusive Development and Ethical AI Governance

A significant part of the problem stems from who builds AI.

Women are dramatically underrepresented in AI research, engineering, and leadership roles, making up less than 22% of AI specialists worldwide, a factor that influences the goals and assumptions baked into systems. When diverse voices are absent, blind spots and narrow definitions of “performance” go unchallenged.

Leaders in ethical AI, such as Rumman Chowdhury, emphasize the importance of defining what “ethical,” “fair,” and “good” actually mean before deployment. Advocates like Buolamwini and Deborah Raji have led research exposing how facial recognition and other tools fail women and people of color, and their work prompted major tech firms to withdraw problematic products from law enforcement use.

Maintain Human Judgment in High‑Stakes Decisions

There are domains where flawed automation can have especially severe consequences, from healthcare diagnostics to criminal justice risk assessments. AI may generate a recommendation, but it lacks moral reasoning, lived experience, and contextual judgment.

In medical settings, for example, algorithmic diagnostic tools can underdiagnose conditions in women because training datasets historically reflect male‑dominated clinical data. Women with atypical symptoms may be misclassified because the system lacks nuance.

Demanding that AI serve as a partner to human judgment, not a substitute, preserves responsibility where it belongs: with people. Always treat automated outputs as provisional, not definitive.

Build Habits of Verification and Cross‑Checking

One reason deferring to AI feels easy is that it delivers fluent, confident language.

But fluency isn’t accuracy. Women in the U.S. who consult AI for career decisions, legal advice, or personal finance should adopt a habit of verification: cross‑referencing with trusted human experts or established sources before acting. This mirrors how experts treat automated outputs in scientific research, as hypotheses to be validated.

Over time, this practice trains critical thinking muscles rather than rusting them. It also helps to identify when a system’s recommendation reflects generic defaults rather than informed judgment. Every interaction becomes an opportunity to refine scrutiny, not abdicate it.

Treat AI as a Tool for Clarifying Values, Not Replacing Them

When AI seems to make “bad decisions,” it’s often because nobody ever clearly defined what “good” looked like in the first place.

AI doesn’t invent ambiguity; it operationalizes whatever objective function it has been given.

Think of AI as a lens that reveals where our own standards were vague or unarticulated, not as an oracle of truth. In doing so, women reclaim agency, ensure that technology reflects their values, and avoid the seductive trap of letting automation shoulder responsibility that properly belongs to human judgment.

Key takeaways

• AI reflects human bias; it doesn’t eliminate it. Automated systems inherit patterns from historical data, which can reproduce gender and racial inequities in hiring, finance, and other decisions.
• Agency matters more than automation. Women must define success criteria, ask clarifying questions, and critically evaluate AI outputs instead of deferring judgment to technology.
• Transparency and accountability are non-negotiable. Legal frameworks, regulatory mandates, and independent audits are tools women can leverage to ensure AI systems operate fairly.
• Representation shapes outcomes. Greater participation of women in AI research, engineering, and leadership reduces blind spots, improves fairness, and ensures systems reflect diverse perspectives.
• Education and verification empower control. Building AI literacy, practicing critical review, and cross-checking automated recommendations preserves human judgment and prevents passive reliance on opaque systems.

Disclosure line: This article was written with the assistance of AI and was subsequently reviewed, revised, and approved by our editorial team.

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