Algorithmic biases can lead to skewed outcomes in technology, affecting areas such as hiring, lending, and law enforcement. These biases often arise from data sets that reflect existing societal inequalities, thereby reinforcing discrimination against certain groups. Consequently, biased algorithms can result in unfair treatment or outcomes, eroding trust in automated systems. Addressing algorithmic biases requires ongoing assessments, diverse data collection, and the implementation of bias-mitigation strategies. By prioritizing fairness and equity, technology developers can minimize negative impacts and create more inclusive solutions.