The term 'algorithmic bias' refers to the systematic and unfair discrimination in outcomes produced by artificial intelligence (AI) systems due to flawed data or design. This bias can arise from unrepresentative training datasets, leading to skewed predictions or recommendations that adversely affect certain groups. Addressing algorithmic bias is critical to ensuring fairness and equity in AI applications. Organizations must implement measures such as diverse data sourcing and rigorous testing to mitigate bias, promoting ethical and responsible AI usage.