AI bias is the presence of unfair or discriminatory outcomes in AI systems.

AI systems are not always neutral and can be influenced by the data they are trained on. Bias in AI can come from various sources, affecting how decisions are made and who they impact. Some key factors that contribute to AI bias include:

• Biased Training Data – If the data used to train AI is unbalanced or favours certain groups, the AI will reflect those biases
• Human Influence – AI models learn from human-made data, meaning they can inherit prejudices and stereotypes
• Lack of Diverse Data – If AI is trained mostly on data from one culture, language, or group, it may not work well for others
• Algorithmic Bias – The way AI processes information can unintentionally favour certain outcomes over others