algorithmic fairness

Bridging the gender data gap: Why representation in AI is a business imperative

According to the UK government, 1 in 6 UK organizations have already implemented AI tools, but bias from unrepresentative data risks perpetuating discrimination and regulatory penalties. The London School of Economics found that large language models like Google's Gemma may introduce gender bias into care decisions. Experts stress that data integrity—through integration, governance, enrichment, and observability—is critical to mitigating bias and ensuring AI outputs are fair and accurate.