By Diana Fernandez
for the 2025 Risky Women Writing Competition, presented in partnership with Ocorian
In a world where algorithms are increasingly calling the shots, it’s ironic that our quest for efficiency could inadvertently reinforce centuries-old biases. Who knew a job rejection could come not from a hiring manager but from an invisible algorithm? Time for “Risky Women” to step up and challenge the system!
We need more Risky Women.
Our imperative is to protect women in the Age of AI.
Imagine being turned down for a job; not by a hiring manager you can challenge, but by an algorithm you’ll never meet.
Across banking, insurance, healthcare and beyond, AI is increasingly deciding:
- Who gets hired?
- Who gets a loan?
- Who gets flagged as “high risk”?
Too often, those algorithms invisibly and insidiously stack the odds against women.
This is not a technology flaw. It is a governance failure.
If we don’t act now, we will hard-wire centuries of discrimination into the very systems designed to make processes more efficient and our lives more effortless.
Our history is becoming our future.
AI doesn’t invent prejudice; it learns it.
Trained on historical datasets; often by teams of broadly white, middle-class men, algorithms copy patterns of inequality and then scale and amplify them.
- Recruitment: Amazon scrapped an AI hiring tool after it consistently downgraded CVs that included the word “women’s,” reflecting the male-dominated tech industry.
- Representation: Just look at the disproportionate number of men completing PhDs in artificial intelligence, and you see how endemic the problem is
- Credit: Studies show women borrowers penalised by credit scoring models, despite lower default rates.
- Healthcare: A US algorithm used to allocate care requirements consistently underestimated Black patients’ needs, with similar patterns emerging in gendered health data.
If bias is not governed and penalised, inequality will scale at a pace we have never seen before.
Why Women in GRC Should Care
The bias within AI tools hits women hardest.
- Hiring algorithms exclude us.
- Credit scoring systems limit our financial independence.
- Compliance monitoring entrenches stereotypes about what a “risky” employee looks like; and being a “Risky Woman” is not a good thing here!
There is a high concentration of women working in risk, audit, legal and compliance roles; a critical line of defence when controls fail.
As someone who has spent years redesigning governance and control functions in global banks, I know that bias is not an abstract problem. It shows up in processes, in data and in the systems we rely on every day.
If we, women, do not form a critical mass of technologists training AI algorithms, if datasets exclude our lived experience and if we are absent from the room when governance guardrails are designed, then bias will go unchallenged.
Diversity is not a “nice to have” in AI oversight. It is a vital control mechanism.
The GRC Imperative
AI bias is not just a social problem. It is a material GRC risk.
- Governance: Boards must demand transparency, sponsor bias testing, and hire diverse talent to oversee AI systems.
- Risk: Biased algorithms create legal, reputational, and regulatory exposure. In financial services, they threaten customer trust and increase systemic risk.
- Compliance: Regulators are moving fast. The EU AI Act will force high-risk AI to be tested for fairness. The UK FCA has flagged AI fairness in credit. The US Equal Employment Opportunity Commission has already warned about algorithmic discrimination in hiring.
This is a live regulatory risk. And we need more Risky Women.
What Needs to Change
To stop bias being coded into our future, fairness must become part of the compliance toolkit.
- Bias audits: Like financial audits, independent reviews of algorithms should be mandatory.
- Diverse teams: Those building and governing AI must reflect the people affected by it.
- AI literacy in GRC: Risk and compliance teams need the skills to ask the right questions of technologists.
- Regulatory teeth: Voluntary frameworks are never enough. Regulators must mandate fairness testing and hold firms accountable.
A Call to Action
AI bias against women is not inevitable. It is a governance challenge waiting to be solved.
And “Risky Women” in GRC are uniquely placed to solve it; because we see the risks, we feel the impact, we have the courage to challenge our boards and we have the tools to demand change.
If we embed fairness into AI now, we can make these systems engines of equity. If we do not, we risk locking in discrimination at scale.
The choice is ours.