AI’S BLIND SPOT: WOMEN — GENDER BIAS 2.0

As AI enters every decision system, we must correct the biases before it’s too late — else the AI age will become Gender Bias 2.0 where patriarchy gets amplified and auto piloted

- NAMRATA KOHLI

When Leena Nair took over as Global CEO of Chanel, she did what many of us do in moments of transition — she turned to AI for clarity. She typed a simple question: “What does Chanel’s leadership look like?”

The answer arrived with supreme confidence: all white. All male. All dressed in black, grey and blue. None of it was true. A luxury brand would never mandate black-and-blue formals, and Chanel’s leadership is far more diverse than the algorithm suggested.

But that is the quiet danger of artificial intelligence. AI does not sneer, interrupt or judge. It offers its answers with serene authority — even when they are factually wrong. And it is precisely this veneer of neutrality that makes algorithmic bias so insidious. AI doesn't end biases; it elegantly recycles them. It performs a kind of algorithmic déja vu — replaying old hierarchies with fresh polish.

Nair’s experience is not an anomaly. Type “CEO” into an image generator and you will see an assembly line of men in suits. “Nurse” yields women. “Engineer” returns men. These systems do not intend to discriminate; they merely mirror the world we have shown them. Code has no gender, but its creators do. So the question becomes urgent: Is AI reinforcing old inequalities or redefining equality for the next generation?

AI learns from the past — and the past isn't gender-neutral. If historical hiring data shows fewer women in leadership, an algorithm trained on it will inevitably “learn” that men make better managers. Most training datasets mirror this imbalance: more male CEOs, male scientists and male “professionals,” and disproportionately more women represented as assistants or “helpers.” When biased history becomes AI’s raw material, biased outcomes become its default.