Machine learning has significantly impacted various industries by enhancing efficiency and automating decision-making processes; however, it faces challenges related to bias, which can lead to unfair predictions and perpetuate societal inequalities. Bias in machine learning arises from systematic errors in algorithms or training data and can be categorized into explicit and implicit biases, influencing perceptions and decisions. Various types of bias, including measurement, omitted variable, aggregation, sampling, linking, and labeling bias, can affect the data used in training models, while algorithmic and user interaction biases can emerge during algorithm design and user feedback. The impacts of bias are evident in sectors like healthcare, criminal justice, employment, and finance, where biased AI models can lead to disparities and inequitable outcomes. To evaluate and mitigate bias, techniques like disparate impact analysis, fairness metrics, diverse data collection, bias-aware algorithms, explainable AI, and regular auditing are employed, with tools like Encord Active aiding in identifying and addressing dataset biases. Mitigating bias requires ongoing efforts and collaboration from data scientists, developers, organizations, and policymakers to ensure AI technologies are fair and ethical, benefiting society without reinforcing discrimination.