Recognising that bias exists and understanding why it does is the first step towards breaking it down. AI systems are fundamentally shaped and flawed, or perfected, by the data they learn from. Machine learning bias mirrors (and perpetuates) human biases as AI systems learn from data that often contains these biases (as well as prompts). This bias then disrupts the fairness and accuracy of AI-driven decisions, results, and answers.
If an AI system learns mainly from data created by one demographic group, the resultant perspectives and opinions may not serve others as well. This is especially relevant within the tech industry, where there is already a lack of diversity. Women only make up around 29% of the sector and ethnic minorities just 22%. This lack of diversity weaves through AI models, which when adopted in business goes on to influence online searches, recruitment drives, and content creation.
Reframing AI to address the bias isn’t just a moral imperative but it is an important step to enhance the quality, reliability, business reputation, and inclusiveness of AI technologies. While a difficult challenge, increasing diversity is essential for AI to provide fair and beneficial outcomes for everyone. As AI’s impact continues to expand into 2025 and beyond, the models defining the future need to represent the full spectrum of human perspectives and experiences.