Algorithmic bias is one of the most prominent ethical and social challenges associated with the development of artificial intelligence. Intelligent systems may produce unfair decisions when they rely on historical data containing patterns of discrimination or when they are designed and tested in a way that disregards the diversity of social groups. The danger of this problem is evident in areas that directly affect individuals' lives, such as employment, education, financial services, and healthcare, where biased algorithms can reduce opportunities for certain groups or treat them unequally. The fact that decisions rely on technology does not necessarily mean they are neutral, as systems are influenced by data quality, the standards set by their developers, and the circumstances in which they are used. Furthermore, the lack of transparency and the difficulty in interpreting some automated decisions can make detecting and correcting discrimination even more complex. Therefore, addressing algorithmic bias requires the use of diverse and representative data, periodic fairness testing, and mechanisms for human review and appeal of impactful decisions. Building responsible AI depends not only on technical accuracy but also on ensuring fairness, transparency, and respect for human rights, so that technology becomes a tool for reducing social inequalities rather than reproducing and deepening them. Al-Mustaqbal University is ranked first among Iraqi private universities.