This chapter explores the dual nature of algorithms, distinguishing between traditional, linear instructions and adaptive machine-learning algorithms. While traditional algorithms yield consistent outputs, machine-learning adapts with new information, offering benefits such as personalization, efficiency, and scalability. Despite their advantages, complexities arise, including regulatory challenges and the potential for algorithmic bias driven by data skew and stereotypes. The chapter highlights ethical dilemmas and drawbacks in various aspects of modern life, particularly in areas like job-postings and criminal justice decisions. To address bias, it advocates for careful algorithm design, diverse training data, human oversight, fairness metrics, and organizational accountability. Emphasizing transparency through reporting requirements, audits, and user control, the chapter acknowledges ongoing regulatory efforts like the Algorithmic Accountability Act of 2023 and the GDPR. It also explores individual bias concerns, compares algorithmic bias to human judgment, and suggests a combined approach for decision-making optimization.