The gender data gap and the need for representation in AI

According to the UK government, 1 in 6 UK organizations have already implemented AI tools.

These technologies offer unprecedented potential to speed up tasks, streamline workflows and facilitate real-time decision-making.

However, despite the widespread benefits, the outputs that AI generates are often taken at face value, with the integrity of their data overlooked.

It must be understood that AI is a product of the data that fuels it. So, if there is a lack of representation in the data powering an AI model, then it is highly likely to start producing biased outputs that risk perpetuating discrimination.

In fact, AI bias is one of the most prominent AI-related issues facing organizations today. To overcome it, organizations must prioritize building trust in their data.

Biased data shapes biased decisions

Bias in AI occurs when the technology unfairly portrays or makes inaccurate assumptions about people because its training data is inaccurate,...

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