Artificial intelligence is often presented as neutral and objective. But is AI really neutral? In practice, it reflects the data it is trained on – and that data is far from neutral.

One of the most concerning issues emerging is misogyny in AI.

From language models to image generators, AI systems can reproduce and even amplify gender bias. This can appear in subtle ways – such as how roles are described – or more obvious patterns in generated content.

Is AI really neutral?

The assumption that AI is impartial is increasingly being challenged. The outputs we see are shaped by training data, design choices, and historical patterns. These patterns are not deliberate, but they challenge the idea that AI is neutral.

For example, prompts around leadership, competence, or personality can reveal underlying assumptions that influence how individuals are portrayed. These outputs can reinforce stereotypes without users even realising it.

Why This Matters

As AI becomes more widely used in business and everyday decision-making, these biases are no longer theoretical. They can influence:

• Hiring and recruitment tools
• Content and communications
• Customer interactions
• Strategic decisions

Without scrutiny, this risks reinforcing outdated assumptions at scale.

Understanding the Risk

The key issue is not just that bias exists, but that it often goes unnoticed. Many users assume AI outputs are reliable, without questioning how those outputs are shaped.

Developing a basic understanding of AI bias and ethics is becoming essential for anyone using these tools, whether casually or professionally.

• Presented by Kelly Thornton
• Hosted by Gregg Hunt

Each session will examine how misogyny in AI appears in practice, using real-world examples and accessible insight relevant to both personal and business use.